# Common Graphs (Graph Generators)¶

All graphs in Sage can be built through the graphs object. In order to build a complete graph on 15 elements, one can do:

sage: g = graphs.CompleteGraph(15)


To get a path with 4 vertices, and the house graph:

sage: p = graphs.PathGraph(4)
sage: h = graphs.HouseGraph()


More interestingly, one can get the list of all graphs that Sage knows how to build by typing graphs. in Sage and then hitting tab.

Basic structures

Small Graphs

A small graph is just a single graph and has no parameter influencing the number of edges or vertices.

Platonic solids (ordered ascending by number of vertices)

Families of graphs

A family of graph is an infinite set of graphs which can be indexed by fixed number of parameters, e.g. two integer parameters. (A method whose name starts with a small letter does not return a single graph object but a graph iterator or a list of graphs or ...)

Chessboard Graphs

Intersection graphs

These graphs are generated by geometric representations. The objects of the representation correspond to the graph vertices and the intersections of objects yield the graph edges.

Random graphs

Graphs with a given degree sequence

Miscellaneous

AUTHORS:

• Robert Miller (2006-11-05): initial version, empty, random, petersen
• Emily Kirkman (2006-11-12): basic structures, node positioning for all constructors
• Emily Kirkman (2006-11-19): docstrings, examples
• William Stein (2006-12-05): Editing.
• Robert Miller (2007-01-16): Cube generation and plotting
• Emily Kirkman (2007-01-16): more basic structures, docstrings
• Emily Kirkman (2007-02-14): added more named graphs
• Robert Miller (2007-06-08-11): Platonic solids, random graphs, graphs with a given degree sequence, random directed graphs
• Robert Miller (2007-10-24): Isomorph free exhaustive generation
• Nathann Cohen (2009-08-12): WorldMap
• Michael Yurko (2009-9-01): added hyperstar, (n,k)-star, n-star, and bubblesort graphs
• Anders Jonsson (2009-10-15): added generalized Petersen graphs
• Harald Schilly and Yann Laigle-Chapuy (2010-03-24): added Fibonacci Tree
• Jason Grout (2010-06-04): cospectral_graphs
• Edward Scheinerman (2010-08-11): RandomTree
• Ed Scheinerman (2010-08-21): added Grotzsch graph and Mycielski graphs
• Minh Van Nguyen (2010-11-26): added more named graphs
• Keshav Kini (2011-02-16): added Shrikhande and Dyck graphs
• David Coudert (2012-02-10): new RandomGNP generator
• David Coudert (2012-08-02): added chessboard graphs: Queen, King, Knight, Bishop, and Rook graphs
• Nico Van Cleemput (2013-05-26): added fullerenes
• Nico Van Cleemput (2013-07-01): added benzenoids
• Birk Eisermann (2013-07-29): new section ‘intersection graphs’, added (random, bounded) tolerance graphs

## Functions and methods¶

class sage.graphs.graph_generators.GraphGenerators

A class consisting of constructors for several common graphs, as well as orderly generation of isomorphism class representatives. See the module's help for a list of supported constructors.

A list of all graphs and graph structures (other than isomorphism class representatives) in this database is available via tab completion. Type “graphs.” and then hit the tab key to see which graphs are available.

The docstrings include educational information about each named graph with the hopes that this class can be used as a reference.

For all the constructors in this class (except the octahedral, dodecahedral, random and empty graphs), the position dictionary is filled to override the spring-layout algorithm.

ORDERLY GENERATION:

graphs(vertices, property=lambda x: True, augment='edges', size=None)


This syntax accesses the generator of isomorphism class representatives. Iterates over distinct, exhaustive representatives.

Also: see the use of the optional nauty package for generating graphs at the nauty_geng() method.

INPUT:

• vertices – natural number.
• property – (default: lambda x: True) any property to be tested on graphs before generation, but note that in general the graphs produced are not the same as those produced by using the property function to filter a list of graphs produced by using the lambda x: True default. The generation process assumes the property has certain characteristics set by the augment argument, and only in the case of inherited properties such that all subgraphs of the relevant kind (for augment='edges' or augment='vertices') of a graph with the property also possess the property will there be no missing graphs. (The property argument is ignored if degree_sequence is specified.)
• augment – (default: 'edges') possible values:
• 'edges' – augments a fixed number of vertices by adding one edge. In this case, all graphs on exactly n=vertices are generated. If for any graph G satisfying the property, every subgraph, obtained from G by deleting one edge but not the vertices incident to that edge, satisfies the property, then this will generate all graphs with that property. If this does not hold, then all the graphs generated will satisfy the property, but there will be some missing.
• 'vertices' – augments by adding a vertex and edges incident to that vertex. In this case, all graphs up to n=vertices are generated. If for any graph G satisfying the property, every subgraph, obtained from G by deleting one vertex and only edges incident to that vertex, satisfies the property, then this will generate all graphs with that property. If this does not hold, then all the graphs generated will satisfy the property, but there will be some missing.
• size – (default: None) the size of the graph to be generated.
• degree_sequence – (default: None) a sequence of non-negative integers, or None. If specified, the generated graphs will have these integers for degrees. In this case, property and size are both ignored.
• loops – (default: False) whether to allow loops in the graph or not.
• implementation – (default: 'c_graph') which underlying implementation to use (see Graph?).
• sparse – (default: True) ignored if implementation is not 'c_graph'.
• copy (boolean) – If set to True (default) this method makes copies of the graphs before returning them. If set to False the method returns the graph it is working on. The second alternative is faster, but modifying any of the graph instances returned by the method may break the function’s behaviour, as it is using these graphs to compute the next ones : only use copy_graph = False when you stick to reading the graphs returned.

EXAMPLES:

Print graphs on 3 or less vertices:

sage: for G in graphs(3, augment='vertices'):
...    print G
Graph on 0 vertices
Graph on 1 vertex
Graph on 2 vertices
Graph on 3 vertices
Graph on 3 vertices
Graph on 3 vertices
Graph on 2 vertices
Graph on 3 vertices


Note that we can also get graphs with underlying Cython implementation:

sage: for G in graphs(3, augment='vertices', implementation='c_graph'):
...    print G
Graph on 0 vertices
Graph on 1 vertex
Graph on 2 vertices
Graph on 3 vertices
Graph on 3 vertices
Graph on 3 vertices
Graph on 2 vertices
Graph on 3 vertices


Print graphs on 3 vertices.

sage: for G in graphs(3):
...    print G
Graph on 3 vertices
Graph on 3 vertices
Graph on 3 vertices
Graph on 3 vertices


Generate all graphs with 5 vertices and 4 edges.

sage: L = graphs(5, size=4)
sage: len(list(L))
6


Generate all graphs with 5 vertices and up to 4 edges.

sage: L = list(graphs(5, lambda G: G.size() <= 4))
sage: len(L)
14
sage: graphs_list.show_graphs(L) # long time


Generate all graphs with up to 5 vertices and up to 4 edges.

sage: L = list(graphs(5, lambda G: G.size() <= 4, augment='vertices'))
sage: len(L)
31
sage: graphs_list.show_graphs(L)              # long time


Generate all graphs with degree at most 2, up to 6 vertices.

sage: property = lambda G: ( max([G.degree(v) for v in G] + [0]) <= 2 )
sage: L = list(graphs(6, property, augment='vertices'))
sage: len(L)
45


Generate all bipartite graphs on up to 7 vertices: (see http://oeis.org/classic/A033995)

sage: L = list( graphs(7, lambda G: G.is_bipartite(), augment='vertices') )
sage: [len([g for g in L if g.order() == i]) for i in [1..7]]
[1, 2, 3, 7, 13, 35, 88]


Generate all bipartite graphs on exactly 7 vertices:

sage: L = list( graphs(7, lambda G: G.is_bipartite()) )
sage: len(L)
88


Generate all bipartite graphs on exactly 8 vertices:

sage: L = list( graphs(8, lambda G: G.is_bipartite()) ) # long time
sage: len(L)                                            # long time
303


Remember that the property argument does not behave as a filter, except for appropriately inheritable properties:

sage: property = lambda G: G.is_vertex_transitive()
sage: len(list(graphs(4, property)))
1
sage: len(filter(property, graphs(4)))
4
sage: property = lambda G: G.is_bipartite()
sage: len(list(graphs(4, property)))
7
sage: len(filter(property, graphs(4)))
7


Generate graphs on the fly: (see http://oeis.org/classic/A000088)

sage: for i in range(0, 7):
...    print len(list(graphs(i)))
1
1
2
4
11
34
156


Generate all simple graphs, allowing loops: (see http://oeis.org/classic/A000666)

sage: L = list(graphs(5,augment='vertices',loops=True))               # long time
sage: for i in [0..5]: print i, len([g for g in L if g.order() == i]) # long time
0 1
1 2
2 6
3 20
4 90
5 544


Generate all graphs with a specified degree sequence (see http://oeis.org/classic/A002851):

sage: for i in [4,6,8]:  # long time (4s on sage.math, 2012)
...       print i, len([g for g in graphs(i, degree_sequence=[3]*i) if g.is_connected()])
4 1
6 2
8 5
sage: for i in [4,6,8]:  # long time (7s on sage.math, 2012)
...       print i, len([g for g in graphs(i, augment='vertices', degree_sequence=[3]*i) if g.is_connected()])
4 1
6 2
8 5

sage: print 10, len([g for g in graphs(10,degree_sequence=[3]*10) if g.is_connected()]) # not tested
10 19


Make sure that the graphs are really independent and the generator survives repeated vertex removal (trac 8458):

sage: for G in graphs(3):
...       G.delete_vertex(0)
...       print(G.order())
2
2
2
2


REFERENCE:

• Brendan D. McKay, Isomorph-Free Exhaustive generation. Journal of Algorithms, Volume 26, Issue 2, February 1998, pages 306-324.
static Balaban10Cage(embedding=1)

Returns the Balaban 10-cage.

The Balaban 10-cage is a 3-regular graph with 70 vertices and 105 edges. See its Wikipedia page.

The default embedding gives a deeper understanding of the graph’s automorphism group. It is divided into 4 layers (each layer being a set of points at equal distance from the drawing’s center). From outside to inside:

• L1: The outer layer (vertices which are the furthest from the origin) is actually the disjoint union of two cycles of length 10.
• L2: The second layer is an independent set of 20 vertices.
• L3: The third layer is a matching on 10 vertices.
• L4: The inner layer (vertices which are the closest from the origin) is also the disjoint union of two cycles of length 10.

This graph is not vertex-transitive, and its vertices are partitioned into 3 orbits: L2, L3, and the union of L1 of L4 whose elements are equivalent.

INPUT:

• embedding – two embeddings are available, and can be selected by setting embedding to be either 1 or 2.

EXAMPLES:

sage: g = graphs.Balaban10Cage()
sage: g.girth()
10
sage: g.chromatic_number()
2
sage: g.diameter()
6
sage: g.is_hamiltonian()
True
sage: g.show(figsize=[10,10])   # long time


TESTS:

sage: graphs.Balaban10Cage(embedding='foo')
Traceback (most recent call last):
...
ValueError: The value of embedding must be 1 or 2.

static Balaban11Cage(embedding=1)

Returns the Balaban 11-cage.

INPUT:

• embedding – three embeddings are available, and can be selected by setting embedding to be 1, 2, or 3.
• The first embedding is the one appearing on page 9 of the Fifth Annual Graph Drawing Contest report [FAGDC]. It separates vertices based on their eccentricity (see eccentricity()).
• The second embedding has been produced just for Sage and is meant to emphasize the automorphism group’s 6 orbits.
• The last embedding is the default one produced by the LCFGraph() constructor.

Note

The vertex labeling changes according to the value of embedding=1.

EXAMPLES:

Basic properties:

sage: g = graphs.Balaban11Cage()
sage: g.order()
112
sage: g.size()
168
sage: g.girth()
11
sage: g.diameter()
8
sage: g.automorphism_group().cardinality()
64


Our many embeddings:

sage: g1 = graphs.Balaban11Cage(embedding=1)
sage: g2 = graphs.Balaban11Cage(embedding=2)
sage: g3 = graphs.Balaban11Cage(embedding=3)
sage: g1.show(figsize=[10,10])   # long time
sage: g2.show(figsize=[10,10])   # long time
sage: g3.show(figsize=[10,10])   # long time


Proof that the embeddings are the same graph:

sage: g1.is_isomorphic(g2) # g2 and g3 are obviously isomorphic
True


TESTS:

sage: graphs.Balaban11Cage(embedding='xyzzy')
Traceback (most recent call last):
...
ValueError: The value of embedding must be 1, 2, or 3.


REFERENCES:

 [FAGDC] Fifth Annual Graph Drawing Contest P. Eaded, J. Marks, P.Mutzel, S. North http://www.merl.com/papers/docs/TR98-16.pdf
static BalancedTree(r, h)

Returns the perfectly balanced tree of height $$h \geq 1$$, whose root has degree $$r \geq 2$$.

The number of vertices of this graph is $$1 + r + r^2 + \cdots + r^h$$, that is, $$\frac{r^{h+1} - 1}{r - 1}$$. The number of edges is one less than the number of vertices.

INPUT:

• r – positive integer $$\geq 2$$. The degree of the root node.
• h – positive integer $$\geq 1$$. The height of the balanced tree.

OUTPUT:

The perfectly balanced tree of height $$h \geq 1$$ and whose root has degree $$r \geq 2$$. A NetworkXError is returned if $$r < 2$$ or $$h < 1$$.

ALGORITHM:

Uses NetworkX.

EXAMPLES:

A balanced tree whose root node has degree $$r = 2$$, and of height $$h = 1$$, has order 3 and size 2:

sage: G = graphs.BalancedTree(2, 1); G
Balanced tree: Graph on 3 vertices
sage: G.order(); G.size()
3
2
sage: r = 2; h = 1
sage: v = 1 + r
sage: v; v - 1
3
2


Plot a balanced tree of height 5, whose root node has degree $$r = 3$$:

sage: G = graphs.BalancedTree(3, 5)
sage: G.show()   # long time


A tree is bipartite. If its vertex set is finite, then it is planar.

sage: r = randint(2, 5); h = randint(1, 7)
sage: T = graphs.BalancedTree(r, h)
sage: T.is_bipartite()
True
sage: T.is_planar()
True
sage: v = (r^(h + 1) - 1) / (r - 1)
sage: T.order() == v
True
sage: T.size() == v - 1
True


TESTS:

Normally we would only consider balanced trees whose root node has degree $$r \geq 2$$, but the construction degenerates gracefully:

sage: graphs.BalancedTree(1, 10)
Balanced tree: Graph on 2 vertices

sage: graphs.BalancedTree(-1, 10)
Balanced tree: Graph on 1 vertex


Similarly, we usually want the tree must have height $$h \geq 1$$ but the algorithm also degenerates gracefully here:

sage: graphs.BalancedTree(3, 0)
Balanced tree: Graph on 1 vertex

sage: graphs.BalancedTree(5, -2)
Balanced tree: Graph on 0 vertices

sage: graphs.BalancedTree(-2,-2)
Balanced tree: Graph on 0 vertices

static BarbellGraph(n1, n2)

Returns a barbell graph with 2*n1 + n2 nodes. The argument n1 must be greater than or equal to 2.

A barbell graph is a basic structure that consists of a path graph of order n2 connecting two complete graphs of order n1 each.

This constructor depends on NetworkX numeric labels. In this case, the n1-th node connects to the path graph from one complete graph and the n1 + n2 + 1-th node connects to the path graph from the other complete graph.

INPUT:

• n1 – integer $$\geq 2$$. The order of each of the two complete graphs.
• n2 – nonnegative integer. The order of the path graph connecting the two complete graphs.

OUTPUT:

A barbell graph of order 2*n1 + n2. A ValueError is returned if n1 < 2 or n2 < 0.

ALGORITHM:

Uses NetworkX.

PLOTTING:

Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, each barbell graph will be displayed with the two complete graphs in the lower-left and upper-right corners, with the path graph connecting diagonally between the two. Thus the n1-th node will be drawn at a 45 degree angle from the horizontal right center of the first complete graph, and the n1 + n2 + 1-th node will be drawn 45 degrees below the left horizontal center of the second complete graph.

EXAMPLES:

Construct and show a barbell graph Bar = 4, Bells = 9:

sage: g = graphs.BarbellGraph(9, 4); g
Barbell graph: Graph on 22 vertices
sage: g.show() # long time


An n1 >= 2, n2 >= 0 barbell graph has order 2*n1 + n2. It has the complete graph on n1 vertices as a subgraph. It also has the path graph on n2 vertices as a subgraph.

sage: n1 = randint(2, 2*10^2)
sage: n2 = randint(0, 2*10^2)
sage: g = graphs.BarbellGraph(n1, n2)
sage: v = 2*n1 + n2
sage: g.order() == v
True
sage: K_n1 = graphs.CompleteGraph(n1)
sage: P_n2 = graphs.PathGraph(n2)
sage: s_K = g.subgraph_search(K_n1, induced=True)
sage: s_P = g.subgraph_search(P_n2, induced=True)
sage: K_n1.is_isomorphic(s_K)
True
sage: P_n2.is_isomorphic(s_P)
True


Create several barbell graphs in a Sage graphics array:

sage: g = []
sage: j = []
sage: for i in range(6):
...       k = graphs.BarbellGraph(i + 2, 4)
...       g.append(k)
...
sage: for i in range(2):
...       n = []
...       for m in range(3):
...           n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
...       j.append(n)
...
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time


TESTS:

The input n1 must be $$\geq 2$$:

sage: graphs.BarbellGraph(1, randint(0, 10^6))
Traceback (most recent call last):
...
ValueError: Invalid graph description, n1 should be >= 2
sage: graphs.BarbellGraph(randint(-10^6, 1), randint(0, 10^6))
Traceback (most recent call last):
...
ValueError: Invalid graph description, n1 should be >= 2


The input n2 must be $$\geq 0$$:

sage: graphs.BarbellGraph(randint(2, 10^6), -1)
Traceback (most recent call last):
...
ValueError: Invalid graph description, n2 should be >= 0
sage: graphs.BarbellGraph(randint(2, 10^6), randint(-10^6, -1))
Traceback (most recent call last):
...
ValueError: Invalid graph description, n2 should be >= 0
sage: graphs.BarbellGraph(randint(-10^6, 1), randint(-10^6, -1))
Traceback (most recent call last):
...
ValueError: Invalid graph description, n1 should be >= 2

static BidiakisCube()

Returns the Bidiakis cube.

EXAMPLES:

The Bidiakis cube is a 3-regular graph having 12 vertices and 18 edges. This means that each vertex has a degree of 3.

sage: g = graphs.BidiakisCube(); g
Bidiakis cube: Graph on 12 vertices
sage: g.show()  # long time
sage: g.order()
12
sage: g.size()
18
sage: g.is_regular(3)
True


It is a Hamiltonian graph with diameter 3 and girth 4:

sage: g.is_hamiltonian()
True
sage: g.diameter()
3
sage: g.girth()
4


It is a planar graph with characteristic polynomial $$(x - 3) (x - 2) (x^4) (x + 1) (x + 2) (x^2 + x - 4)^2$$ and chromatic number 3:

sage: g.is_planar()
True
sage: bool(g.characteristic_polynomial() == expand((x - 3) * (x - 2) * (x^4) * (x + 1) * (x + 2) * (x^2 + x - 4)^2))
True
sage: g.chromatic_number()
3

static BiggsSmithGraph(embedding=1)

Returns the Biggs-Smith graph.

INPUT:

• embedding – two embeddings are available, and can be selected by setting embedding to be 1 or 2.

EXAMPLES:

Basic properties:

sage: g = graphs.BiggsSmithGraph()
sage: g.order()
102
sage: g.size()
153
sage: g.girth()
9
sage: g.diameter()
7
sage: g.automorphism_group().cardinality()
2448
sage: g.show(figsize=[10, 10])   # long time


The other embedding:

sage: graphs.BiggsSmithGraph(embedding=2).show()


TESTS:

sage: graphs.BiggsSmithGraph(embedding='xyzzy')
Traceback (most recent call last):
...
ValueError: The value of embedding must be 1 or 2.


Returns the $$d$$-dimensional Bishop Graph with prescribed dimensions.

The 2-dimensional Bishop Graph of parameters $$n$$ and $$m$$ is a graph with $$nm$$ vertices in which each vertex represents a square in an $$n \times m$$ chessboard, and each edge corresponds to a legal move by a bishop.

The $$d$$-dimensional Bishop Graph with $$d >= 2$$ has for vertex set the cells of a $$d$$-dimensional grid with prescribed dimensions, and each edge corresponds to a legal move by a bishop in any pairs of dimensions.

The Bishop Graph is not connected.

INPUTS:

• dim_list – an iterable object (list, set, dict) providing the dimensions $$n_1, n_2, \ldots, n_d$$, with $$n_i \geq 1$$, of the chessboard.
• radius – (default: None) by setting the radius to a positive integer, one may decrease the power of the bishop to at most radius steps.
• relabel – (default: False) a boolean set to True if vertices must be relabeled as integers.

EXAMPLES:

The (n,m)-Bishop Graph is not connected:

sage: G = graphs.BishopGraph( [3, 4] )
sage: G.is_connected()
False


The Bishop Graph can be obtained from Knight Graphs:

sage: for d in xrange(3,12):   # long time
....:     H = Graph()
....:     for r in xrange(1,d+1):
....:         if not B.is_isomorphic(H):
....:            print "that's not good!"

static BlanusaFirstSnarkGraph()

Returns the first Blanusa Snark Graph.

The Blanusa graphs are two snarks on 18 vertices and 27 edges. For more information on them, see the Wikipedia article Blanusa_snarks.

EXAMPLES:

sage: g = graphs.BlanusaFirstSnarkGraph()
sage: g.order()
18
sage: g.size()
27
sage: g.diameter()
4
sage: g.girth()
5
sage: g.automorphism_group().cardinality()
8

static BlanusaSecondSnarkGraph()

Returns the second Blanusa Snark Graph.

The Blanusa graphs are two snarks on 18 vertices and 27 edges. For more information on them, see the Wikipedia article Blanusa_snarks.

EXAMPLES:

sage: g = graphs.BlanusaSecondSnarkGraph()
sage: g.order()
18
sage: g.size()
27
sage: g.diameter()
4
sage: g.girth()
5
sage: g.automorphism_group().cardinality()
4

static BrinkmannGraph()

Returns the Brinkmann graph.

EXAMPLES:

The Brinkmann graph is a 4-regular graph having 21 vertices and 42 edges. This means that each vertex has degree 4.

sage: G = graphs.BrinkmannGraph(); G
Brinkmann graph: Graph on 21 vertices
sage: G.show()  # long time
sage: G.order()
21
sage: G.size()
42
sage: G.is_regular(4)
True


It is an Eulerian graph with radius 3, diameter 3, and girth 5.

sage: G.is_eulerian()
True
3
sage: G.diameter()
3
sage: G.girth()
5


The Brinkmann graph is also Hamiltonian with chromatic number 4:

sage: G.is_hamiltonian()
True
sage: G.chromatic_number()
4


Its automorphism group is isomorphic to $$D_7$$:

sage: ag = G.automorphism_group()
sage: ag.is_isomorphic(DihedralGroup(7))
True

static BrouwerHaemersGraph()

Returns the Brouwer-Haemers Graph.

The Brouwer-Haemers is the only strongly regular graph of parameters $$(81,20,1,6)$$. It is build in Sage as the Affine Orthogonal graph $$VO^-(6,3)$$. For more information on this graph, see its corresponding page on Andries Brouwer’s website.

EXAMPLE:

sage: g = graphs.BrouwerHaemersGraph()
sage: g
Brouwer-Haemers: Graph on 81 vertices


It is indeed strongly regular with parameters $$(81,20,1,6)$$:

sage: g.is_strongly_regular(parameters = True) # long time
(81, 20, 1, 6)


Its has as eigenvalues $$20,2$$ and $$-7$$:

sage: set(g.spectrum()) == {20,2,-7}
True

static BubbleSortGraph(n)

Returns the bubble sort graph $$B(n)$$.

The vertices of the bubble sort graph are the set of permutations on $$n$$ symbols. Two vertices are adjacent if one can be obtained from the other by swapping the labels in the $$i$$-th and $$(i+1)$$-th positions for $$1 \leq i \leq n-1$$. In total, $$B(n)$$ has order $$n!$$. Thus, the order of $$B(n)$$ increases according to $$f(n) = n!$$.

INPUT:

• n – positive integer. The number of symbols to permute.

OUTPUT:

The bubble sort graph $$B(n)$$ on $$n$$ symbols. If $$n < 1$$, a ValueError is returned.

EXAMPLES:

sage: g = graphs.BubbleSortGraph(4); g
Bubble sort: Graph on 24 vertices
sage: g.plot() # long time


The bubble sort graph on $$n = 1$$ symbol is the trivial graph $$K_1$$:

sage: graphs.BubbleSortGraph(1)
Bubble sort: Graph on 1 vertex


If $$n \geq 1$$, then the order of $$B(n)$$ is $$n!$$:

sage: n = randint(1, 8)
sage: g = graphs.BubbleSortGraph(n)
sage: g.order() == factorial(n)
True


TESTS:

Input n must be positive:

sage: graphs.BubbleSortGraph(0)
Traceback (most recent call last):
...
ValueError: Invalid number of symbols to permute, n should be >= 1
sage: graphs.BubbleSortGraph(randint(-10^6, 0))
Traceback (most recent call last):
...
ValueError: Invalid number of symbols to permute, n should be >= 1


AUTHORS:

• Michael Yurko (2009-09-01)
static BuckyBall()

Create the Bucky Ball graph.

This graph is a 3-regular 60-vertex planar graph. Its vertices and edges correspond precisely to the carbon atoms and bonds in buckminsterfullerene. When embedded on a sphere, its 12 pentagon and 20 hexagon faces are arranged exactly as the sections of a soccer ball.

EXAMPLES:

The Bucky Ball is planar.

sage: g = graphs.BuckyBall()
sage: g.is_planar()
True


The Bucky Ball can also be created by extracting the 1-skeleton of the Bucky Ball polyhedron, but this is much slower.

sage: g = polytopes.buckyball().vertex_graph()
sage: g.remove_loops()
sage: h = graphs.BuckyBall()
sage: g.is_isomorphic(h)
True


The graph is returned along with an attractive embedding.

sage: g = graphs.BuckyBall()
sage: g.plot(vertex_labels=False, vertex_size=10).show() # long time

static BullGraph()

Returns a bull graph with 5 nodes.

A bull graph is named for its shape. It’s a triangle with horns. This constructor depends on NetworkX numeric labeling. For more information, see this Wikipedia article on the bull graph.

PLOTTING:

Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, the bull graph is drawn as a triangle with the first node (0) on the bottom. The second and third nodes (1 and 2) complete the triangle. Node 3 is the horn connected to 1 and node 4 is the horn connected to node 2.

ALGORITHM:

Uses NetworkX.

EXAMPLES:

Construct and show a bull graph:

sage: g = graphs.BullGraph(); g
Bull graph: Graph on 5 vertices
sage: g.show() # long time


The bull graph has 5 vertices and 5 edges. Its radius is 2, its diameter 3, and its girth 3. The bull graph is planar with chromatic number 3 and chromatic index also 3.

sage: g.order(); g.size()
5
5
2
3
3
sage: g.chromatic_number()
3


The bull graph has chromatic polynomial $$x(x - 2)(x - 1)^3$$ and Tutte polynomial $$x^4 + x^3 + x^2 y$$. Its characteristic polynomial is $$x(x^2 - x - 3)(x^2 + x - 1)$$, which follows from the definition of characteristic polynomials for graphs, i.e. $$\det(xI - A)$$, where $$x$$ is a variable, $$A$$ the adjacency matrix of the graph, and $$I$$ the identity matrix of the same dimensions as $$A$$.

sage: chrompoly = g.chromatic_polynomial()
sage: bool(expand(x * (x - 2) * (x - 1)^3) == chrompoly)
True
sage: charpoly = g.characteristic_polynomial()
[0 1 1 0 0]
[1 0 1 1 0]
[1 1 0 0 1]
[0 1 0 0 0]
[0 0 1 0 0]
sage: Id = identity_matrix(ZZ, M.nrows())
sage: D = x*Id - M
sage: bool(D.determinant() == charpoly)
True
sage: bool(expand(x * (x^2 - x - 3) * (x^2 + x - 1)) == charpoly)
True

static ButterflyGraph()

Returns the butterfly graph.

Let $$C_3$$ be the cycle graph on 3 vertices. The butterfly or bowtie graph is obtained by joining two copies of $$C_3$$ at a common vertex, resulting in a graph that is isomorphic to the friendship graph $$F_2$$. For more information, see this Wikipedia article on the butterfly graph.

EXAMPLES:

The butterfly graph is a planar graph on 5 vertices and having 6 edges.

sage: G = graphs.ButterflyGraph(); G
Butterfly graph: Graph on 5 vertices
sage: G.show()  # long time
sage: G.is_planar()
True
sage: G.order()
5
sage: G.size()
6


It has diameter 2, girth 3, and radius 1.

sage: G.diameter()
2
sage: G.girth()
3
1


The butterfly graph is Eulerian, with chromatic number 3.

sage: G.is_eulerian()
True
sage: G.chromatic_number()
3

static CameronGraph()

Returns the Cameron graph.

The Cameron graph is strongly regular with parameters $$v = 231, k = 30, \lambda = 9, \mu = 3$$.

EXAMPLES:

sage: g = graphs.CameronGraph()
sage: g.order()
231
sage: g.size()
3465
sage: g.is_strongly_regular(parameters = True) # long time
(231, 30, 9, 3)


Returns a Graph built on a $$d$$-dimensional chessboard with prescribed dimensions and interconnections.

This function allows to generate many kinds of graphs corresponding to legal movements on a $$d$$-dimensional chessboard: Queen Graph, King Graph, Knight Graphs, Bishop Graph, and many generalizations. It also allows to avoid redondant code.

INPUTS:

• dim_list – an iterable object (list, set, dict) providing the dimensions $$n_1, n_2, \ldots, n_d$$, with $$n_i \geq 1$$, of the chessboard.
• rook – (default: True) boolean value indicating if the chess piece is able to move as a rook, that is at any distance along a dimension.
• rook_radius – (default: None) integer value restricting the rook-like movements to distance at most $$rook_radius$$.
• bishop – (default: True) boolean value indicating if the chess piece is able to move like a bishop, that is along diagonals.
• bishop_radius – (default: None) integer value restricting the bishop-like movements to distance at most $$bishop_radius$$.
• knight – (default: True) boolean value indicating if the chess piece is able to move like a knight.
• knight_x – (default: 1) integer indicating the number on steps the chess piece moves in one dimension when moving like a knight.
• knight_y – (default: 2) integer indicating the number on steps the chess piece moves in the second dimension when moving like a knight.
• relabel – (default: False) a boolean set to True if vertices must be relabeled as integers.

OUTPUTS:

• A Graph build on a $$d$$-dimensional chessboard with prescribed dimensions, and with edges according given parameters.
• A string encoding the dimensions. This is mainly useful for providing names to graphs.

EXAMPLES:

A $$(2,2)$$-King Graph is isomorphic to the complete graph on 4 vertices:

sage: G, _ = graphs.ChessboardGraphGenerator( [2,2] )
sage: G.is_isomorphic( graphs.CompleteGraph(4) )
True


A Rook’s Graph in 2 dimensions is isomporphic to the cartesian product of 2 complete graphs:

sage: G, _ = graphs.ChessboardGraphGenerator( [3,4], rook=True, rook_radius=None, bishop=False, knight=False )
sage: H = ( graphs.CompleteGraph(3) ).cartesian_product( graphs.CompleteGraph(4) )
sage: G.is_isomorphic(H)
True


TESTS:

Giving dimensions less than 2:

sage: graphs.ChessboardGraphGenerator( [0, 2] )
Traceback (most recent call last):
...
ValueError: The dimensions must be positive integers larger than 1.


Giving non integer dimensions:

sage: graphs.ChessboardGraphGenerator( [4.5, 2] )
Traceback (most recent call last):
...
ValueError: The dimensions must be positive integers larger than 1.


Giving too few dimensions:

sage: graphs.ChessboardGraphGenerator( [2] )
Traceback (most recent call last):
...
ValueError: The chessboard must have at least 2 dimensions.


Giving a non-iterable object as first parameter:

sage: graphs.ChessboardGraphGenerator( 2, 3 )
Traceback (most recent call last):
...
TypeError: The first parameter must be an iterable object.


sage: graphs.ChessboardGraphGenerator( [2, 3], rook=True, rook_radius=0 )
Traceback (most recent call last):
...
ValueError: The rook_radius must be either None or have an integer value >= 1.


Giving wrong values for knights movements:

sage: graphs.ChessboardGraphGenerator( [2, 3], rook=False, bishop=False, knight=True, knight_x=1, knight_y=-1 )
Traceback (most recent call last):
...
ValueError: The knight_x and knight_y values must be integers of value >= 1.

static ChvatalGraph()

Returns the Chvatal graph.

Chvatal graph is one of the few known graphs to satisfy Grunbaum’s conjecture that for every m, n, there is an m-regular, m-chromatic graph of girth at least n. For more information, see this Wikipedia article on the Chvatal graph.

EXAMPLES:

The Chvatal graph has 12 vertices and 24 edges. It is a 4-regular, 4-chromatic graph with radius 2, diameter 2, and girth 4.

sage: G = graphs.ChvatalGraph(); G
Chvatal graph: Graph on 12 vertices
sage: G.order(); G.size()
12
24
sage: G.degree()
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
sage: G.chromatic_number()
4
2
2
4


Returns a circulant graph with n nodes.

A circulant graph has the property that the vertex $$i$$ is connected with the vertices $$i+j$$ and $$i-j$$ for each j in adj.

INPUT:

• n - number of vertices in the graph
• adjacency - the list of j values

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, each circulant graph will be displayed with the first (0) node at the top, with the rest following in a counterclockwise manner.

EXAMPLES: Compare plotting using the predefined layout and networkx:

sage: import networkx
sage: n = networkx.cycle_graph(23)
sage: spring23 = Graph(n)
sage: posdict23 = graphs.CirculantGraph(23,2)
sage: spring23.show() # long time
sage: posdict23.show() # long time


We next view many cycle graphs as a Sage graphics array. First we use the CirculantGraph constructor, which fills in the position dictionary:

sage: g = []
sage: j = []
sage: for i in range(9):
...    k = graphs.CirculantGraph(i+3,i)
...    g.append(k)
...
sage: for i in range(3):
...    n = []
...    for m in range(3):
...        n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
...    j.append(n)
...
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time


Compare to plotting with the spring-layout algorithm:

sage: g = []
sage: j = []
sage: for i in range(9):
...    spr = networkx.cycle_graph(i+3)
...    k = Graph(spr)
...    g.append(k)
...
sage: for i in range(3):
...    n = []
...    for m in range(3):
...        n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
...    j.append(n)
...
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time


Passing a 1 into adjacency should give the cycle.

sage: graphs.CirculantGraph(6,1)==graphs.CycleGraph(6)
True
sage: graphs.CirculantGraph(7,[1,3]).edges(labels=false)
[(0, 1),
(0, 3),
(0, 4),
(0, 6),
(1, 2),
(1, 4),
(1, 5),
(2, 3),
(2, 5),
(2, 6),
(3, 4),
(3, 6),
(4, 5),
(5, 6)]


Returns a circular ladder graph with 2*n nodes.

A Circular ladder graph is a ladder graph that is connected at the ends, i.e.: a ladder bent around so that top meets bottom. Thus it can be described as two parallel cycle graphs connected at each corresponding node pair.

This constructor depends on NetworkX numeric labels.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, the circular ladder graph is displayed as an inner and outer cycle pair, with the first n nodes drawn on the inner circle. The first (0) node is drawn at the top of the inner-circle, moving clockwise after that. The outer circle is drawn with the (n+1)th node at the top, then counterclockwise as well.

EXAMPLES: Construct and show a circular ladder graph with 26 nodes

sage: g = graphs.CircularLadderGraph(13)
sage: g.show() # long time


Create several circular ladder graphs in a Sage graphics array

sage: g = []
sage: j = []
sage: for i in range(9):
....:    g.append(k)
sage: for i in range(3):
....:    n = []
....:    for m in range(3):
....:        n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:    j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time

static ClawGraph()

Returns a claw graph.

A claw graph is named for its shape. It is actually a complete bipartite graph with (n1, n2) = (1, 3).

PLOTTING: See CompleteBipartiteGraph.

EXAMPLES: Show a Claw graph

sage: (graphs.ClawGraph()).show() # long time


Inspect a Claw graph

sage: G = graphs.ClawGraph()
sage: G
Claw graph: Graph on 4 vertices

static ClebschGraph()

Return the Clebsch graph.

EXAMPLES:

sage: g = graphs.ClebschGraph()
sage: g.automorphism_group().cardinality()
1920
sage: g.girth()
4
sage: g.chromatic_number()
4
sage: g.diameter()
2
sage: g.show(figsize=[10, 10]) # long time

static CompleteBipartiteGraph(n1, n2)

Returns a Complete Bipartite Graph sized n1+n2, with each of the nodes [0,(n1-1)] connected to each of the nodes [n1,(n2-1)] and vice versa.

A Complete Bipartite Graph is a graph with its vertices partitioned into two groups, V1 and V2. Each v in V1 is connected to every v in V2, and vice versa.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, each complete bipartite graph will be displayed with the first n1 nodes on the top row (at y=1) from left to right. The remaining n2 nodes appear at y=0, also from left to right. The shorter row (partition with fewer nodes) is stretched to the same length as the longer row, unless the shorter row has 1 node; in which case it is centered. The x values in the plot are in domain [0,maxn1,n2].

In the Complete Bipartite graph, there is a visual difference in using the spring-layout algorithm vs. the position dictionary used in this constructor. The position dictionary flattens the graph and separates the partitioned nodes, making it clear which nodes an edge is connected to. The Complete Bipartite graph plotted with the spring-layout algorithm tends to center the nodes in n1 (see spring_med in examples below), thus overlapping its nodes and edges, making it typically hard to decipher.

Filling the position dictionary in advance adds O(n) to the constructor. Feel free to race the constructors below in the examples section. The much larger difference is the time added by the spring-layout algorithm when plotting. (Also shown in the example below). The spring model is typically described as $$O(n^3)$$, as appears to be the case in the NetworkX source code.

EXAMPLES: Two ways of constructing the complete bipartite graph, using different layout algorithms:

sage: import networkx
sage: n = networkx.complete_bipartite_graph(389,157); spring_big = Graph(n)   # long time
sage: posdict_big = graphs.CompleteBipartiteGraph(389,157)                    # long time


Compare the plotting:

sage: n = networkx.complete_bipartite_graph(11,17)
sage: spring_med = Graph(n)
sage: posdict_med = graphs.CompleteBipartiteGraph(11,17)


Notice here how the spring-layout tends to center the nodes of n1

sage: spring_med.show() # long time
sage: posdict_med.show() # long time


View many complete bipartite graphs with a Sage Graphics Array, with this constructor (i.e., the position dictionary filled):

sage: g = []
sage: j = []
sage: for i in range(9):
....:     k = graphs.CompleteBipartiteGraph(i+1,4)
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time


We compare to plotting with the spring-layout algorithm:

sage: g = []
sage: j = []
sage: for i in range(9):
....:     spr = networkx.complete_bipartite_graph(i+1,4)
....:     k = Graph(spr)
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time


Trac ticket #12155:

sage: graphs.CompleteBipartiteGraph(5,6).complement()
complement(Complete bipartite graph): Graph on 11 vertices

static CompleteGraph(n)

Returns a complete graph on n nodes.

A Complete Graph is a graph in which all nodes are connected to all other nodes.

This constructor is dependent on vertices numbered 0 through n-1 in NetworkX complete_graph()

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, each complete graph will be displayed with the first (0) node at the top, with the rest following in a counterclockwise manner.

In the complete graph, there is a big difference visually in using the spring-layout algorithm vs. the position dictionary used in this constructor. The position dictionary flattens the graph, making it clear which nodes an edge is connected to. But the complete graph offers a good example of how the spring-layout works. The edges push outward (everything is connected), causing the graph to appear as a 3-dimensional pointy ball. (See examples below).

EXAMPLES: We view many Complete graphs with a Sage Graphics Array, first with this constructor (i.e., the position dictionary filled):

sage: g = []
sage: j = []
sage: for i in range(9):
....:     k = graphs.CompleteGraph(i+3)
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time


We compare to plotting with the spring-layout algorithm:

sage: import networkx
sage: g = []
sage: j = []
sage: for i in range(9):
....:     spr = networkx.complete_graph(i+3)
....:     k = Graph(spr)
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time


Compare the constructors (results will vary)

sage: import networkx
sage: t = cputime()
sage: n = networkx.complete_graph(389); spring389 = Graph(n)
sage: cputime(t)           # random
0.59203700000000126
sage: t = cputime()
sage: posdict389 = graphs.CompleteGraph(389)
sage: cputime(t)           # random
0.6680419999999998


We compare plotting:

sage: import networkx
sage: n = networkx.complete_graph(23)
sage: spring23 = Graph(n)
sage: posdict23 = graphs.CompleteGraph(23)
sage: spring23.show() # long time
sage: posdict23.show() # long time

static CompleteMultipartiteGraph(l)

Returns a complete multipartite graph.

INPUT:

• l – a list of integers : the respective sizes of the components.

EXAMPLE:

A complete tripartite graph with sets of sizes $$5, 6, 8$$:

sage: g = graphs.CompleteMultipartiteGraph([5, 6, 8]); g
Multipartite Graph with set sizes [5, 6, 8]: Graph on 19 vertices


It clearly has a chromatic number of 3:

sage: g.chromatic_number()
3

static CoxeterGraph()

Return the Coxeter graph.

EXAMPLES:

sage: g = graphs.CoxeterGraph()
sage: g.automorphism_group().cardinality()
336
sage: g.girth()
7
sage: g.chromatic_number()
3
sage: g.diameter()
4
sage: g.show(figsize=[10, 10]) # long time

static CubeGraph(n)

Returns the hypercube in $$n$$ dimensions.

The hypercube in $$n$$ dimension is build upon the binary strings on $$n$$ bits, two of them being adjacent if they differ in exactly one bit. Hence, the distance between two vertices in the hypercube is the Hamming distance.

EXAMPLES:

The distance between $$0100110$$ and $$1011010$$ is $$5$$, as expected

sage: g = graphs.CubeGraph(7)
sage: g.distance('0100110','1011010')
5


Plot several $$n$$-cubes in a Sage Graphics Array

sage: g = []
sage: j = []
sage: for i in range(6):
...    k = graphs.CubeGraph(i+1)
...    g.append(k)
...
sage: for i in range(2):
...    n = []
...    for m in range(3):
...        n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
...    j.append(n)
...
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show(figsize=[6,4]) # long time


Use the plot options to display larger $$n$$-cubes

sage: g = graphs.CubeGraph(9)
sage: g.show(figsize=[12,12],vertex_labels=False, vertex_size=20) # long time


AUTHORS:

• Robert Miller
static CycleGraph(n)

Returns a cycle graph with n nodes.

A cycle graph is a basic structure which is also typically called an n-gon.

This constructor is dependent on vertices numbered 0 through n-1 in NetworkX cycle_graph()

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, each cycle graph will be displayed with the first (0) node at the top, with the rest following in a counterclockwise manner.

The cycle graph is a good opportunity to compare efficiency of filling a position dictionary vs. using the spring-layout algorithm for plotting. Because the cycle graph is very symmetric, the resulting plots should be similar (in cases of small n).

EXAMPLES: Compare plotting using the predefined layout and networkx:

sage: import networkx
sage: n = networkx.cycle_graph(23)
sage: spring23 = Graph(n)
sage: posdict23 = graphs.CycleGraph(23)
sage: spring23.show() # long time
sage: posdict23.show() # long time


We next view many cycle graphs as a Sage graphics array. First we use the CycleGraph constructor, which fills in the position dictionary:

sage: g = []
sage: j = []
sage: for i in range(9):
....:     k = graphs.CycleGraph(i+3)
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time


Compare to plotting with the spring-layout algorithm:

sage: g = []
sage: j = []
sage: for i in range(9):
....:     spr = networkx.cycle_graph(i+3)
....:     k = Graph(spr)
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time

static DegreeSequence(deg_sequence)

Returns a graph with the given degree sequence. Raises a NetworkX error if the proposed degree sequence cannot be that of a graph.

Graph returned is the one returned by the Havel-Hakimi algorithm, which constructs a simple graph by connecting vertices of highest degree to other vertices of highest degree, resorting the remaining vertices by degree and repeating the process. See Theorem 1.4 in [CharLes1996].

INPUT:

• deg_sequence - a list of integers with each entry corresponding to the degree of a different vertex.

EXAMPLES:

sage: G = graphs.DegreeSequence([3,3,3,3])
sage: G.edges(labels=False)
[(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]
sage: G.show()  # long time

sage: G = graphs.DegreeSequence([3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3])
sage: G.show()  # long time

sage: G = graphs.DegreeSequence([4,4,4,4,4,4,4,4])
sage: G.show()  # long time

sage: G = graphs.DegreeSequence([1,2,3,4,3,4,3,2,3,2,1])
sage: G.show()  # long time


REFERENCE:

 [CharLes1996] Chartrand, G. and Lesniak, L.: Graphs and Digraphs. Chapman and Hall/CRC, 1996.
static DegreeSequenceBipartite(s1, s2)

Returns a bipartite graph whose two sets have the given degree sequences.

Given two different sequences of degrees $$s_1$$ and $$s_2$$, this functions returns ( if possible ) a bipartite graph on sets $$A$$ and $$B$$ such that the vertices in $$A$$ have $$s_1$$ as their degree sequence, while $$s_2$$ is the degree sequence of the vertices in $$B$$.

INPUT:

• s_1 – list of integers corresponding to the degree sequence of the first set.
• s_2 – list of integers corresponding to the degree sequence of the second set.

ALGORITHM:

This function works through the computation of the matrix given by the Gale-Ryser theorem, which is in this case the adjacency matrix of the bipartite graph.

EXAMPLES:

If we are given as sequences [2,2,2,2,2] and [5,5] we are given as expected the complete bipartite graph $$K_{2,5}$$

sage: g = graphs.DegreeSequenceBipartite([2,2,2,2,2],[5,5])
sage: g.is_isomorphic(graphs.CompleteBipartiteGraph(5,2))
True


Some sequences being incompatible if, for example, their sums are different, the functions raises a ValueError when no graph corresponding to the degree sequences exists.

sage: g = graphs.DegreeSequenceBipartite([2,2,2,2,1],[5,5])
Traceback (most recent call last):
...
ValueError: There exists no bipartite graph corresponding to the given degree sequences


TESTS:

Trac ticket #12155:

sage: graphs.DegreeSequenceBipartite([2,2,2,2,2],[5,5]).complement()
complement(): Graph on 7 vertices

static DegreeSequenceConfigurationModel(deg_sequence, seed=None)

Returns a random pseudograph with the given degree sequence. Raises a NetworkX error if the proposed degree sequence cannot be that of a graph with multiple edges and loops.

One requirement is that the sum of the degrees must be even, since every edge must be incident with two vertices.

INPUT:

• deg_sequence - a list of integers with each entry corresponding to the expected degree of a different vertex.
• seed - for the random number generator.

EXAMPLES:

sage: G = graphs.DegreeSequenceConfigurationModel([1,1])
[0 1]
[1 0]


Note: as of this writing, plotting of loops and multiple edges is not supported, and the output is allowed to contain both types of edges.

sage: G = graphs.DegreeSequenceConfigurationModel([3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3])
sage: G.edges(labels=False)
[(0, 2), (0, 10), (0, 15), (1, 6), (1, 16), (1, 17), (2, 5), (2, 19), (3, 7), (3, 14), (3, 14), (4, 9), (4, 13), (4, 19), (5, 6), (5, 15), (6, 11), (7, 11), (7, 17), (8, 11), (8, 18), (8, 19), (9, 12), (9, 13), (10, 15), (10, 18), (12, 13), (12, 16), (14, 17), (16, 18)]
sage: G.show()  # long time


REFERENCE:

 [Newman2003] Newman, M.E.J. The Structure and function of complex networks, SIAM Review vol. 45, no. 2 (2003), pp. 167-256.
static DegreeSequenceExpected(deg_sequence, seed=None)

Returns a random graph with expected given degree sequence. Raises a NetworkX error if the proposed degree sequence cannot be that of a graph.

One requirement is that the sum of the degrees must be even, since every edge must be incident with two vertices.

INPUT:

• deg_sequence - a list of integers with each entry corresponding to the expected degree of a different vertex.
• seed - for the random number generator.

EXAMPLE:

sage: G = graphs.DegreeSequenceExpected([1,2,3,2,3])
sage: G.edges(labels=False)
[(0, 2), (0, 3), (1, 1), (1, 4), (2, 3), (2, 4), (3, 4), (4, 4)]
sage: G.show()  # long time


REFERENCE:

 [ChungLu2002] Chung, Fan and Lu, L. Connected components in random graphs with given expected degree sequences. Ann. Combinatorics (6), 2002 pp. 125-145.
static DegreeSequenceTree(deg_sequence)

Returns a tree with the given degree sequence. Raises a NetworkX error if the proposed degree sequence cannot be that of a tree.

Since every tree has one more vertex than edge, the degree sequence must satisfy len(deg_sequence) - sum(deg_sequence)/2 == 1.

INPUT:

• deg_sequence - a list of integers with each entry corresponding to the expected degree of a different vertex.

EXAMPLE:

sage: G = graphs.DegreeSequenceTree([3,1,3,3,1,1,1,2,1])
sage: G.show()  # long time

static DesarguesGraph()

Returns the Desargues graph.

PLOTTING: The layout chosen is the same as on the cover of [1].

EXAMPLE:

sage: D = graphs.DesarguesGraph()
sage: L = graphs.LCFGraph(20,[5,-5,9,-9],5)
sage: D.is_isomorphic(L)
True
sage: D.show()  # long time


REFERENCE:

static DiamondGraph()

Returns a diamond graph with 4 nodes.

A diamond graph is a square with one pair of diagonal nodes connected.

This constructor depends on NetworkX numeric labeling.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, the diamond graph is drawn as a diamond, with the first node on top, second on the left, third on the right, and fourth on the bottom; with the second and third node connected.

EXAMPLES: Construct and show a diamond graph

sage: g = graphs.DiamondGraph()
sage: g.show() # long time

static DodecahedralGraph()

Returns a Dodecahedral graph (with 20 nodes)

The dodecahedral graph is cubic symmetric, so the spring-layout algorithm will be very effective for display. It is dual to the icosahedral graph.

PLOTTING: The Dodecahedral graph should be viewed in 3 dimensions. We chose to use the default spring-layout algorithm here, so that multiple iterations might yield a different point of reference for the user. We hope to add rotatable, 3-dimensional viewing in the future. In such a case, a string argument will be added to select the flat spring-layout over a future implementation.

EXAMPLES: Construct and show a Dodecahedral graph

sage: g = graphs.DodecahedralGraph()
sage: g.show() # long time


Create several dodecahedral graphs in a Sage graphics array They will be drawn differently due to the use of the spring-layout algorithm

sage: g = []
sage: j = []
sage: for i in range(9):
....:     k = graphs.DodecahedralGraph()
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time

static DorogovtsevGoltsevMendesGraph(n)

Construct the n-th generation of the Dorogovtsev-Goltsev-Mendes graph.

EXAMPLE:

sage: G = graphs.DorogovtsevGoltsevMendesGraph(8)
sage: G.size()
6561


REFERENCE:

• [1] Dorogovtsev, S. N., Goltsev, A. V., and Mendes, J. F. F., Pseudofractal scale-free web, Phys. Rev. E 066122 (2002).
static DoubleStarSnark()

Returns the double star snark.

The double star snark is a 3-regular graph on 30 vertices. See the Wikipedia page on the double star snark.

EXAMPLES:

sage: g = graphs.DoubleStarSnark()
sage: g.order()
30
sage: g.size()
45
sage: g.chromatic_number()
3
sage: g.is_hamiltonian()
False
sage: g.automorphism_group().cardinality()
80
sage: g.show()

static DurerGraph()

Returns the Dürer graph.

EXAMPLES:

The Dürer graph is named after Albrecht Dürer. It is a planar graph with 12 vertices and 18 edges.

sage: G = graphs.DurerGraph(); G
Durer graph: Graph on 12 vertices
sage: G.is_planar()
True
sage: G.order()
12
sage: G.size()
18


The Dürer graph has chromatic number 3, diameter 4, and girth 3.

sage: G.chromatic_number()
3
sage: G.diameter()
4
sage: G.girth()
3


Its automorphism group is isomorphic to $$D_6$$.

sage: ag = G.automorphism_group()
sage: ag.is_isomorphic(DihedralGroup(6))
True

static DyckGraph()

Returns the Dyck graph.

For more information, see the MathWorld article on the Dyck graph or the Wikipedia article on the Dyck graph.

EXAMPLES:

The Dyck graph was defined by Walther von Dyck in 1881. It has $$32$$ vertices and $$48$$ edges, and is a cubic graph (regular of degree $$3$$):

sage: G = graphs.DyckGraph(); G
Dyck graph: Graph on 32 vertices
sage: G.order()
32
sage: G.size()
48
sage: G.is_regular()
True
sage: G.is_regular(3)
True


It is non-planar and Hamiltonian, as well as bipartite (making it a bicubic graph):

sage: G.is_planar()
False
sage: G.is_hamiltonian()
True
sage: G.is_bipartite()
True


It has radius $$5$$, diameter $$5$$, and girth $$6$$:

sage: G.radius()
5
sage: G.diameter()
5
sage: G.girth()
6


Its chromatic number is $$2$$ and its automorphism group is of order $$192$$:

sage: G.chromatic_number()
2
sage: G.automorphism_group().cardinality()
192


It is a non-integral graph as it has irrational eigenvalues:

sage: G.characteristic_polynomial().factor()
(x - 3) * (x + 3) * (x - 1)^9 * (x + 1)^9 * (x^2 - 5)^6


It is a toroidal graph, and its embedding on a torus is dual to an embedding of the Shrikhande graph (ShrikhandeGraph).

static EllinghamHorton54Graph()

Returns the Ellingham-Horton 54-graph.

EXAMPLE:

This graph is 3-regular:

sage: g = graphs.EllinghamHorton54Graph()
sage: g.is_regular(k=3)
True


It is 3-connected and bipartite:

sage: g.vertex_connectivity() # not tested - too long
3
sage: g.is_bipartite()
True


It is not Hamiltonian:

sage: g.is_hamiltonian() # not tested - too long
False


... and it has a nice drawing

sage: g.show(figsize=[10, 10]) # not tested - too long


TESTS:

sage: g.show() # long time

static EllinghamHorton78Graph()

Returns the Ellingham-Horton 78-graph.

EXAMPLE:

This graph is 3-regular:

sage: g = graphs.EllinghamHorton78Graph()
sage: g.is_regular(k=3)
True


It is 3-connected and bipartite:

sage: g.vertex_connectivity() # not tested - too long
3
sage: g.is_bipartite()
True


It is not Hamiltonian:

sage: g.is_hamiltonian() # not tested - too long
False


... and it has a nice drawing

sage: g.show(figsize=[10,10]) # not tested - too long


TESTS:

sage: g.show(figsize=[10, 10]) # not tested - too long

static EmptyGraph()

Returns an empty graph (0 nodes and 0 edges).

This is useful for constructing graphs by adding edges and vertices individually or in a loop.

PLOTTING: When plotting, this graph will use the default spring-layout algorithm, unless a position dictionary is specified.

EXAMPLES: Add one vertex to an empty graph and then show:

sage: empty1 = graphs.EmptyGraph()
0
sage: empty1.show() # long time


Use for loops to build a graph from an empty graph:

sage: empty2 = graphs.EmptyGraph()
sage: for i in range(5):
0
1
2
3
4
sage: for i in range(3):
sage: for i in range(4)[1:]:
sage: empty2.show() # long time

static ErreraGraph()

Returns the Errera graph.

EXAMPLES:

The Errera graph is named after Alfred Errera. It is a planar graph on 17 vertices and having 45 edges.

sage: G = graphs.ErreraGraph(); G
Errera graph: Graph on 17 vertices
sage: G.is_planar()
True
sage: G.order()
17
sage: G.size()
45


The Errera graph is Hamiltonian with radius 3, diameter 4, girth 3, and chromatic number 4.

sage: G.is_hamiltonian()
True
3
sage: G.diameter()
4
sage: G.girth()
3
sage: G.chromatic_number()
4


Each vertex degree is either 5 or 6. That is, if $$f$$ counts the number of vertices of degree 5 and $$s$$ counts the number of vertices of degree 6, then $$f + s$$ is equal to the order of the Errera graph.

sage: D = G.degree_sequence()
sage: D.count(5) + D.count(6) == G.order()
True


The automorphism group of the Errera graph is isomorphic to the dihedral group of order 20.

sage: ag = G.automorphism_group()
sage: ag.is_isomorphic(DihedralGroup(10))
True

static FibonacciTree(n)

Returns the graph of the Fibonacci Tree $$F_{i}$$ of order $$n$$. $$F_{i}$$ is recursively defined as the a tree with a root vertex and two attached child trees $$F_{i-1}$$ and $$F_{i-2}$$, where $$F_{1}$$ is just one vertex and $$F_{0}$$ is empty.

INPUT:

• n - the recursion depth of the Fibonacci Tree

EXAMPLES:

sage: g = graphs.FibonacciTree(3)
sage: g.is_tree()
True

sage: l1 = [ len(graphs.FibonacciTree(_)) + 1 for _ in range(6) ]
sage: l2 = list(fibonacci_sequence(2,8))
sage: l1 == l2
True


AUTHORS:

• Harald Schilly and Yann Laigle-Chapuy (2010-03-25)
static FlowerSnark()

Returns a Flower Snark.

A flower snark has 20 vertices. It is part of the class of biconnected cubic graphs with edge chromatic number = 4, known as snarks. (i.e.: the Petersen graph). All snarks are not Hamiltonian, non-planar and have Petersen graph graph minors.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, the nodes are drawn 0-14 on the outer circle, and 15-19 in an inner pentagon.

REFERENCES:

EXAMPLES: Inspect a flower snark:

sage: F = graphs.FlowerSnark()
sage: F
Flower Snark: Graph on 20 vertices
sage: F.graph6_string()
'ShCGHC@?GGg@?@?Gp?K??C?CA?G?_G?Cc'


Now show it:

sage: F.show() # long time

static FoldedCubeGraph(n)

Returns the folded cube graph of order $$2^{n-1}$$.

The folded cube graph on $$2^{n-1}$$ vertices can be obtained from a cube graph on $$2^n$$ vertices by merging together opposed vertices. Alternatively, it can be obtained from a cube graph on $$2^{n-1}$$ vertices by adding an edge between opposed vertices. This second construction is the one produced by this method.

EXAMPLES:

The folded cube graph of order five is the Clebsch graph:

sage: fc = graphs.FoldedCubeGraph(5)
sage: clebsch = graphs.ClebschGraph()
sage: fc.is_isomorphic(clebsch)
True

static FolkmanGraph()

Returns the Folkman graph.

EXAMPLE:

sage: g = graphs.FolkmanGraph()
sage: g.order()
20
sage: g.size()
40
sage: g.diameter()
4
sage: g.girth()
4
sage: g.charpoly().factor()
(x - 4) * (x + 4) * x^10 * (x^2 - 6)^4
sage: g.chromatic_number()
2
sage: g.is_eulerian()
True
sage: g.is_hamiltonian()
True
sage: g.is_vertex_transitive()
False
sage: g.is_bipartite()
True

static FosterGraph()

Returns the Foster graph.

EXAMPLE:

sage: g = graphs.FosterGraph()
sage: g.order()
90
sage: g.size()
135
sage: g.diameter()
8
sage: g.girth()
10
sage: g.automorphism_group().cardinality()
4320
sage: g.is_hamiltonian()
True

static FranklinGraph()

Returns the Franklin graph.

EXAMPLES:

The Franklin graph is named after Philip Franklin. It is a 3-regular graph on 12 vertices and having 18 edges.

sage: G = graphs.FranklinGraph(); G
Franklin graph: Graph on 12 vertices
sage: G.is_regular(3)
True
sage: G.order()
12
sage: G.size()
18


The Franklin graph is a Hamiltonian, bipartite graph with radius 3, diameter 3, and girth 4.

sage: G.is_hamiltonian()
True
sage: G.is_bipartite()
True
3
sage: G.diameter()
3
sage: G.girth()
4


It is a perfect, triangle-free graph having chromatic number 2.

sage: G.is_perfect()
True
sage: G.is_triangle_free()
True
sage: G.chromatic_number()
2

static FriendshipGraph(n)

Returns the friendship graph $$F_n$$.

The friendship graph is also known as the Dutch windmill graph. Let $$C_3$$ be the cycle graph on 3 vertices. Then $$F_n$$ is constructed by joining $$n \geq 1$$ copies of $$C_3$$ at a common vertex. If $$n = 1$$, then $$F_1$$ is isomorphic to $$C_3$$ (the triangle graph). If $$n = 2$$, then $$F_2$$ is the butterfly graph, otherwise known as the bowtie graph. For more information, see this Wikipedia article on the friendship graph.

INPUT:

• n – positive integer; the number of copies of $$C_3$$ to use in constructing $$F_n$$.

OUTPUT:

• The friendship graph $$F_n$$ obtained from $$n$$ copies of the cycle graph $$C_3$$.

EXAMPLES:

The first few friendship graphs.

sage: A = []; B = []
sage: for i in range(9):
...       g = graphs.FriendshipGraph(i + 1)
...       A.append(g)
sage: for i in range(3):
...       n = []
...       for j in range(3):
...           n.append(A[3*i + j].plot(vertex_size=20, vertex_labels=False))
...       B.append(n)
sage: G = sage.plot.graphics.GraphicsArray(B)
sage: G.show()  # long time


For $$n = 1$$, the friendship graph $$F_1$$ is isomorphic to the cycle graph $$C_3$$, whose visual representation is a triangle.

sage: G = graphs.FriendshipGraph(1); G
Friendship graph: Graph on 3 vertices
sage: G.show()  # long time
sage: G.is_isomorphic(graphs.CycleGraph(3))
True


For $$n = 2$$, the friendship graph $$F_2$$ is isomorphic to the butterfly graph, otherwise known as the bowtie graph.

sage: G = graphs.FriendshipGraph(2); G
Friendship graph: Graph on 5 vertices
sage: G.is_isomorphic(graphs.ButterflyGraph())
True


If $$n \geq 1$$, then the friendship graph $$F_n$$ has $$2n + 1$$ vertices and $$3n$$ edges. It has radius 1, diameter 2, girth 3, and chromatic number 3. Furthermore, $$F_n$$ is planar and Eulerian.

sage: n = randint(1, 10^3)
sage: G = graphs.FriendshipGraph(n)
sage: G.order() == 2*n + 1
True
sage: G.size() == 3*n
True
1
sage: G.diameter()
2
sage: G.girth()
3
sage: G.chromatic_number()
3
sage: G.is_planar()
True
sage: G.is_eulerian()
True


TESTS:

The input n must be a positive integer.

sage: graphs.FriendshipGraph(randint(-10^5, 0))
Traceback (most recent call last):
...
ValueError: n must be a positive integer

static FruchtGraph()

Returns a Frucht Graph.

A Frucht graph has 12 nodes and 18 edges. It is the smallest cubic identity graph. It is planar and it is Hamiltonian.

This constructor is dependent on NetworkX’s numeric labeling.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, the first seven nodes are on the outer circle, with the next four on an inner circle and the last in the center.

REFERENCES:

EXAMPLES:

sage: FRUCHT = graphs.FruchtGraph()
sage: FRUCHT
Frucht graph: Graph on 12 vertices
sage: FRUCHT.graph6_string()
'KhCKM?_EGK?L'
sage: (graphs.FruchtGraph()).show() # long time

static FuzzyBallGraph(partition, q)

Construct a Fuzzy Ball graph with the integer partition partition and q extra vertices.

Let $$q$$ be an integer and let $$m_1,m_2,...,m_k$$ be a set of positive integers. Let $$n=q+m_1+...+m_k$$. The Fuzzy Ball graph with partition $$m_1,m_2,...,m_k$$ and $$q$$ extra vertices is the graph constructed from the graph $$G=K_n$$ by attaching, for each $$i=1,2,...,k$$, a new vertex $$a_i$$ to $$m_i$$ distinct vertices of $$G$$.

For given positive integers $$k$$ and $$m$$ and nonnegative integer $$q$$, the set of graphs FuzzyBallGraph(p, q) for all partitions $$p$$ of $$m$$ with $$k$$ parts are cospectral with respect to the normalized Laplacian.

EXAMPLES:

sage: graphs.FuzzyBallGraph([3,1],2).adjacency_matrix()
[0 1 1 1 1 1 1 0]
[1 0 1 1 1 1 1 0]
[1 1 0 1 1 1 1 0]
[1 1 1 0 1 1 0 1]
[1 1 1 1 0 1 0 0]
[1 1 1 1 1 0 0 0]
[1 1 1 0 0 0 0 0]
[0 0 0 1 0 0 0 0]


Pick positive integers $$m$$ and $$k$$ and a nonnegative integer $$q$$. All the FuzzyBallGraphs constructed from partitions of $$m$$ with $$k$$ parts should be cospectral with respect to the normalized Laplacian:

sage: m=4; q=2; k=2
sage: g_list=[graphs.FuzzyBallGraph(p,q) for p in Partitions(m, length=k)]
sage: set([g.laplacian_matrix(normalized=True).charpoly() for g in g_list])  # long time (7s on sage.math, 2011)
set([x^8 - 8*x^7 + 4079/150*x^6 - 68689/1350*x^5 + 610783/10800*x^4 - 120877/3240*x^3 + 1351/100*x^2 - 931/450*x])

static GeneralizedPetersenGraph(n, k)

Returns a generalized Petersen graph with $$2n$$ nodes. The variables $$n$$, $$k$$ are integers such that $$n>2$$ and $$0<k\leq\lfloor(n-1)$$/$$2\rfloor$$

For $$k=1$$ the result is a graph isomorphic to the circular ladder graph with the same $$n$$. The regular Petersen Graph has $$n=5$$ and $$k=2$$. Other named graphs that can be described using this notation include the Desargues graph and the Moebius-Kantor graph.

INPUT:

• n - the number of nodes is $$2*n$$.
• k - integer $$0<k\leq\lfloor(n-1)$$/$$2\rfloor$$. Decides how inner vertices are connected.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, the generalized Petersen graphs are displayed as an inner and outer cycle pair, with the first n nodes drawn on the outer circle. The first (0) node is drawn at the top of the outer-circle, moving counterclockwise after that. The inner circle is drawn with the (n)th node at the top, then counterclockwise as well.

EXAMPLES: For $$k=1$$ the resulting graph will be isomorphic to a circular ladder graph.

sage: g = graphs.GeneralizedPetersenGraph(13,1)
sage: g.is_isomorphic(g2)
True


The Desargues graph:

sage: g = graphs.GeneralizedPetersenGraph(10,3)
sage: g.girth()
6
sage: g.is_bipartite()
True


AUTHORS:

• Anders Jonsson (2009-10-15)
static GoldnerHararyGraph()

Return the Goldner-Harary graph.

EXAMPLES:

The Goldner-Harary graph is named after A. Goldner and Frank Harary. It is a planar graph having 11 vertices and 27 edges.

sage: G = graphs.GoldnerHararyGraph(); G
Goldner-Harary graph: Graph on 11 vertices
sage: G.is_planar()
True
sage: G.order()
11
sage: G.size()
27


The Goldner-Harary graph is chordal with radius 2, diameter 2, and girth 3.

sage: G.is_chordal()
True
2
sage: G.diameter()
2
sage: G.girth()
3


Its chromatic number is 4 and its automorphism group is isomorphic to the dihedral group $$D_6$$.

sage: G.chromatic_number()
4
sage: ag = G.automorphism_group()
sage: ag.is_isomorphic(DihedralGroup(6))
True

static GrayGraph(embedding=1)

Returns the Gray graph.

INPUT:

• embedding – two embeddings are available, and can be selected by setting embedding to 1 or 2.

EXAMPLES:

sage: g = graphs.GrayGraph()
sage: g.order()
54
sage: g.size()
81
sage: g.girth()
8
sage: g.diameter()
6
sage: g.show(figsize=[10, 10])   # long time
sage: graphs.GrayGraph(embedding = 2).show(figsize=[10, 10])   # long time


TESTS:

sage: graphs.GrayGraph(embedding = 3)
Traceback (most recent call last):
...
ValueError: The value of embedding must be 1, 2, or 3.

static Grid2dGraph(n1, n2)

Returns a $$2$$-dimensional grid graph with $$n_1n_2$$ nodes ($$n_1$$ rows and $$n_2$$ columns).

A 2d grid graph resembles a $$2$$ dimensional grid. All inner nodes are connected to their $$4$$ neighbors. Outer (non-corner) nodes are connected to their $$3$$ neighbors. Corner nodes are connected to their 2 neighbors.

This constructor depends on NetworkX numeric labels.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, nodes are labelled in (row, column) pairs with $$(0, 0)$$ in the top left corner. Edges will always be horizontal and vertical - another advantage of filling the position dictionary.

EXAMPLES: Construct and show a grid 2d graph Rows = $$5$$, Columns = $$7$$

sage: g = graphs.Grid2dGraph(5,7)
sage: g.show() # long time


TESTS:

Senseless input:

sage: graphs.Grid2dGraph(5,0)
Traceback (most recent call last):
...
ValueError: Parameters n1 and n2 must be positive integers !
sage: graphs.Grid2dGraph(-1,0)
Traceback (most recent call last):
...
ValueError: Parameters n1 and n2 must be positive integers !


The graph name contains the dimension:

sage: g = graphs.Grid2dGraph(5,7)
sage: g.name()
'2D Grid Graph for [5, 7]'

static GridGraph(dim_list)

Returns an n-dimensional grid graph.

INPUT:

• dim_list - a list of integers representing the number of nodes to extend in each dimension.

PLOTTING: When plotting, this graph will use the default spring-layout algorithm, unless a position dictionary is specified.

EXAMPLES:

sage: G = graphs.GridGraph([2,3,4])
sage: G.show()  # long time

sage: C = graphs.CubeGraph(4)
sage: G = graphs.GridGraph([2,2,2,2])
sage: C.show()  # long time
sage: G.show()  # long time


TESTS:

The graph name contains the dimension:

sage: g = graphs.GridGraph([5, 7])
sage: g.name()
'Grid Graph for [5, 7]'
sage: g = graphs.GridGraph([2, 3, 4])
sage: g.name()
'Grid Graph for [2, 3, 4]'
sage: g = graphs.GridGraph([2, 4, 3])
sage: g.name()
'Grid Graph for [2, 4, 3]'


All dimensions must be positive integers:

sage: g = graphs.GridGraph([2,-1,3])
Traceback (most recent call last):
...
ValueError: All dimensions must be positive integers !

static GrotzschGraph()

Returns the Grötzsch graph.

The Grötzsch graph is an example of a triangle-free graph with chromatic number equal to 4. For more information, see this Wikipedia article on Grötzsch graph.

REFERENCE:

EXAMPLES:

The Grötzsch graph is named after Herbert Grötzsch. It is a Hamiltonian graph with 11 vertices and 20 edges.

sage: G = graphs.GrotzschGraph(); G
Grotzsch graph: Graph on 11 vertices
sage: G.is_hamiltonian()
True
sage: G.order()
11
sage: G.size()
20


The Grötzsch graph is triangle-free and having radius 2, diameter 2, and girth 4.

sage: G.is_triangle_free()
True
2
sage: G.diameter()
2
sage: G.girth()
4


Its chromatic number is 4 and its automorphism group is isomorphic to the dihedral group $$D_5$$.

sage: G.chromatic_number()
4
sage: ag = G.automorphism_group()
sage: ag.is_isomorphic(DihedralGroup(5))
True

static HallJankoGraph(from_string=True)

Returns the Hall-Janko graph.

The construction used to generate this graph in Sage is by a 100-point permutation representation of the Janko group $$J_2$$, as described in version 3 of the ATLAS of Finite Group representations, in particular on the page ATLAS: J2 – Permutation representation on 100 points.

INPUT:

• from_string (boolean) – whether to build the graph from its sparse6 string or through GAP. The two methods return the same graph though doing it through GAP takes more time. It is set to True by default.

EXAMPLES:

sage: g = graphs.HallJankoGraph()
sage: g.is_regular(36)
True
sage: g.is_vertex_transitive()
True


Is it really strongly regular with parameters 14, 12?

sage: nu = set(g.neighbors(0))
sage: for v in range(1, 100):
....:     if v in nu:
....:         expected = 14
....:     else:
....:         expected = 12
....:     nv = set(g.neighbors(v))
....:     if len(nu & nv) != expected:
....:         print "Something is wrong here!!!"
....:         break


Some other properties that we know how to check:

sage: g.diameter()
2
sage: g.girth()
3
sage: factor(g.characteristic_polynomial())
(x - 36) * (x - 6)^36 * (x + 4)^63


TESTS:

sage: gg = graphs.HallJankoGraph(from_string=False) # long time
sage: g == gg # long time
True

static HanoiTowerGraph(pegs, disks, labels=True, positions=True)

Returns the graph whose vertices are the states of the Tower of Hanoi puzzle, with edges representing legal moves between states.

INPUT:

• pegs - the number of pegs in the puzzle, 2 or greater
• disks - the number of disks in the puzzle, 1 or greater
• labels - default: True, if True the graph contains more meaningful labels, see explanation below. For large instances, turn off labels for much faster creation of the graph.
• positions - default: True, if True the graph contains layout information. This creates a planar layout for the case of three pegs. For large instances, turn off layout information for much faster creation of the graph.

OUTPUT:

The Tower of Hanoi puzzle has a certain number of identical pegs and a certain number of disks, each of a different radius. Initially the disks are all on a single peg, arranged in order of their radii, with the largest on the bottom.

The goal of the puzzle is to move the disks to any other peg, arranged in the same order. The one constraint is that the disks resident on any one peg must always be arranged with larger radii lower down.

The vertices of this graph represent all the possible states of this puzzle. Each state of the puzzle is a tuple with length equal to the number of disks, ordered by largest disk first. The entry of the tuple is the peg where that disk resides. Since disks on a given peg must go down in size as we go up the peg, this totally describes the state of the puzzle.

For example (2,0,0) means the large disk is on peg 2, the medium disk is on peg 0, and the small disk is on peg 0 (and we know the small disk must be above the medium disk). We encode these tuples as integers with a base equal to the number of pegs, and low-order digits to the right.

Two vertices are adjacent if we can change the puzzle from one state to the other by moving a single disk. For example, (2,0,0) is adjacent to (2,0,1) since we can move the small disk off peg 0 and onto (the empty) peg 1. So the solution to a 3-disk puzzle (with at least two pegs) can be expressed by the shortest path between (0,0,0) and (1,1,1). For more on this representation of the graph, or its properties, see [ARETT-DOREE].

For greatest speed we create graphs with integer vertices, where we encode the tuples as integers with a base equal to the number of pegs, and low-order digits to the right. So for example, in a 3-peg puzzle with 5 disks, the state (1,2,0,1,1) is encoded as $$1\ast 3^4 + 2\ast 3^3 + 0\ast 3^2 + 1\ast 3^1 + 1\ast 3^0 = 139$$.

For smaller graphs, the labels that are the tuples are informative, but slow down creation of the graph. Likewise computing layout information also incurs a significant speed penalty. For maximum speed, turn off labels and layout and decode the vertices explicitly as needed. The sage.rings.integer.Integer.digits() with the padsto option is a quick way to do this, though you may want to reverse the list that is output.

PLOTTING:

The layout computed when positions = True will look especially good for the three-peg case, when the graph is known to be planar. Except for two small cases on 4 pegs, the graph is otherwise not planar, and likely there is a better way to layout the vertices.

EXAMPLES:

A classic puzzle uses 3 pegs. We solve the 5 disk puzzle using integer labels and report the minimum number of moves required. Note that $$3^5-1$$ is the state where all 5 disks are on peg 2.

sage: H = graphs.HanoiTowerGraph(3, 5, labels=False, positions=False)
sage: H.distance(0, 3^5-1)
31


A slightly larger instance.

sage: H = graphs.HanoiTowerGraph(4, 6, labels=False, positions=False)
sage: H.num_verts()
4096
sage: H.distance(0, 4^6-1)
17


For a small graph, labels and layout information can be useful. Here we explicitly list a solution as a list of states.

sage: H = graphs.HanoiTowerGraph(3, 3, labels=True, positions=True)
sage: H.shortest_path((0,0,0), (1,1,1))
[(0, 0, 0), (0, 0, 1), (0, 2, 1), (0, 2, 2), (1, 2, 2), (1, 2, 0), (1, 1, 0), (1, 1, 1)]


Some facts about this graph with $$p$$ pegs and $$d$$ disks:

• only automorphisms are the “obvious” ones - renumber the pegs.
• chromatic number is less than or equal to $$p$$
• independence number is $$p^{d-1}$$
sage: H = graphs.HanoiTowerGraph(3,4,labels=False,positions=False)
sage: H.automorphism_group().is_isomorphic(SymmetricGroup(3))
True
sage: H.chromatic_number()
3
sage: len(H.independent_set()) == 3^(4-1)
True


TESTS:

It is an error to have just one peg (or less).

sage: graphs.HanoiTowerGraph(1, 5)
Traceback (most recent call last):
...
ValueError: Pegs for Tower of Hanoi graph should be two or greater (not 1)


It is an error to have zero disks (or less).

sage: graphs.HanoiTowerGraph(2, 0)
Traceback (most recent call last):
...
ValueError: Disks for Tower of Hanoi graph should be one or greater (not 0)


Citations

 [ARETT-DOREE] Arett, Danielle and Doree, Suzanne “Coloring and counting on the Hanoi graphs” Mathematics Magazine, Volume 83, Number 3, June 2010, pages 200-9

AUTHOR:

• Rob Beezer, (2009-12-26), with assistance from Su Doree
static HararyGraph(k, n)

Returns the Harary graph on $$n$$ vertices and connectivity $$k$$, where $$2 \leq k < n$$.

A $$k$$-connected graph $$G$$ on $$n$$ vertices requires the minimum degree $$\delta(G)\geq k$$, so the minimum number of edges $$G$$ should have is $$\lceil kn/2\rceil$$. Harary graphs achieve this lower bound, that is, Harary graphs are minimal $$k$$-connected graphs on $$n$$ vertices.

The construction provided uses the method CirculantGraph. For more details, see the book D. B. West, Introduction to Graph Theory, 2nd Edition, Prentice Hall, 2001, p. 150–151; or the MathWorld article on Harary graphs.

EXAMPLES:

Harary graphs $$H_{k,n}$$:

sage: h = graphs.HararyGraph(5,9); h
Harary graph 5, 9: Graph on 9 vertices
sage: h.order()
9
sage: h.size()
23
sage: h.vertex_connectivity()
5


TESTS:

Connectivity of some Harary graphs:

sage: n=10
sage: for k in range(2,n):
...       g = graphs.HararyGraph(k,n)
...       if k != g.vertex_connectivity():
...          print "Connectivity of Harary graphs not satisfied."

static HarriesGraph(embedding=1)

Returns the Harries Graph.

The Harries graph is a Hamiltonian 3-regular graph on 70 vertices. See the Wikipedia page on the Harries graph.

The default embedding here is to emphasize the graph’s 4 orbits. This graph actually has a funny construction. The following procedure gives an idea of it, though not all the adjacencies are being properly defined.

1. Take two disjoint copies of a Petersen graph. Their vertices will form an orbit of the final graph.
2. Subdivide all the edges once, to create 15+15=30 new vertices, which together form another orbit.
3. Create 15 vertices, each of them linked to 2 corresponding vertices of the previous orbit, one in each of the two subdivided Petersen graphs. At the end of this step all vertices from the previous orbit have degree 3, and the only vertices of degree 2 in the graph are those that were just created.
4. Create 5 vertices connected only to the ones from the previous orbit so that the graph becomes 3-regular.

INPUT:

• embedding – two embeddings are available, and can be selected by setting embedding to 1 or 2.

EXAMPLES:

sage: g = graphs.HarriesGraph()
sage: g.order()
70
sage: g.size()
105
sage: g.girth()
10
sage: g.diameter()
6
sage: g.show(figsize=[10, 10])   # long time
sage: graphs.HarriesGraph(embedding=2).show(figsize=[10, 10])   # long time


TESTS:

sage: graphs.HarriesGraph(embedding=3)
Traceback (most recent call last):
...
ValueError: The value of embedding must be 1 or 2.

static HarriesWongGraph(embedding=1)

Returns the Harries-Wong Graph.

The default embedding is an attempt to emphasize the graph’s 8 (!!!) different orbits. In order to understand this better, one can picture the graph as being built in the following way:

1. One first creates a 3-dimensional cube (8 vertices, 12 edges), whose vertices define the first orbit of the final graph.
2. The edges of this graph are subdivided once, to create 12 new vertices which define a second orbit.
3. The edges of the graph are subdivided once more, to create 24 new vertices giving a third orbit.
4. 4 vertices are created and made adjacent to the vertices of the second orbit so that they have degree 3. These 4 vertices also define a new orbit.
5. In order to make the vertices from the third orbit 3-regular (they all miss one edge), one creates a binary tree on 1 + 3 + 6 + 12 vertices. The leaves of this new tree are made adjacent to the 12 vertices of the third orbit, and the graph is now 3-regular. This binary tree contributes 4 new orbits to the Harries-Wong graph.

INPUT:

• embedding – two embeddings are available, and can be selected by setting embedding to 1 or 2.

EXAMPLES:

sage: g = graphs.HarriesWongGraph()
sage: g.order()
70
sage: g.size()
105
sage: g.girth()
10
sage: g.diameter()
6
sage: orbits = g.automorphism_group(orbits=True)[-1]
sage: g.show(figsize=[15, 15], partition=orbits)   # long time


Alternative embedding:

sage: graphs.HarriesWongGraph(embedding=2).show()


TESTS:

sage: graphs.HarriesWongGraph(embedding=3)
Traceback (most recent call last):
...
ValueError: The value of embedding must be 1 or 2.

static HeawoodGraph()

Returns a Heawood graph.

The Heawood graph is a cage graph that has 14 nodes. It is a cubic symmetric graph. (See also the Moebius-Kantor graph). It is nonplanar and Hamiltonian. It has diameter = 3, radius = 3, girth = 6, chromatic number = 2. It is 4-transitive but not 5-transitive.

This constructor is dependent on NetworkX’s numeric labeling.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, the nodes are positioned in a circular layout with the first node appearing at the top, and then continuing counterclockwise.

REFERENCES:

EXAMPLES:

sage: H = graphs.HeawoodGraph()
sage: H
Heawood graph: Graph on 14 vertices
sage: H.graph6_string()
'MhEGHC@AI?_PC@_G_'
sage: (graphs.HeawoodGraph()).show() # long time

static HerschelGraph()

Returns the Herschel graph.

EXAMPLES:

The Herschel graph is named after Alexander Stewart Herschel. It is a planar, bipartite graph with 11 vertices and 18 edges.

sage: G = graphs.HerschelGraph(); G
Herschel graph: Graph on 11 vertices
sage: G.is_planar()
True
sage: G.is_bipartite()
True
sage: G.order()
11
sage: G.size()
18


The Herschel graph is a perfect graph with radius 3, diameter 4, and girth 4.

sage: G.is_perfect()
True
3
sage: G.diameter()
4
sage: G.girth()
4


Its chromatic number is 2 and its automorphism group is isomorphic to the dihedral group $$D_6$$.

sage: G.chromatic_number()
2
sage: ag = G.automorphism_group()
sage: ag.is_isomorphic(DihedralGroup(6))
True

static HexahedralGraph()

Returns a hexahedral graph (with 8 nodes).

A regular hexahedron is a 6-sided cube. The hexahedral graph corresponds to the connectivity of the vertices of the hexahedron. This graph is equivalent to a 3-cube.

PLOTTING: The hexahedral graph should be viewed in 3 dimensions. We chose to use the default spring-layout algorithm here, so that multiple iterations might yield a different point of reference for the user. We hope to add rotatable, 3-dimensional viewing in the future. In such a case, a string argument will be added to select the flat spring-layout over a future implementation.

EXAMPLES: Construct and show a Hexahedral graph

sage: g = graphs.HexahedralGraph()
sage: g.show() # long time


Create several hexahedral graphs in a Sage graphics array. They will be drawn differently due to the use of the spring-layout algorithm.

sage: g = []
sage: j = []
sage: for i in range(9):
....:     k = graphs.HexahedralGraph()
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time

static HigmanSimsGraph(relabel=True)

The Higman-Sims graph is a remarkable strongly regular graph of degree 22 on 100 vertices. For example, it can be split into two sets of 50 vertices each, so that each half induces a subgraph isomorphic to the Hoffman-Singleton graph (HoffmanSingletonGraph()). This can be done in 352 ways (see [BROUWER-HS-2009]).

Its most famous property is that the automorphism group has an index 2 subgroup which is one of the 26 sporadic groups. [HIGMAN1968]

The construction used here follows [HAFNER2004].

INPUT:

• relabel - default: True. If True the vertices will be labeled with consecutive integers. If False the labels are strings that are three digits long. “xyz” means the vertex is in group x (zero through three), pentagon or pentagram y (zero through four), and is vertex z (zero through four) of that pentagon or pentagram. See [HAFNER2004] for more.

OUTPUT:

The Higman-Sims graph.

EXAMPLES:

A split into the first 50 and last 50 vertices will induce two copies of the Hoffman-Singleton graph, and we illustrate another such split, which is obvious based on the construction used.

sage: H = graphs.HigmanSimsGraph()
sage: A = H.subgraph(range(0,50))
sage: B = H.subgraph(range(50,100))
sage: K = graphs.HoffmanSingletonGraph()
sage: K.is_isomorphic(A) and K.is_isomorphic(B)
True
sage: C = H.subgraph(range(25,75))
sage: D = H.subgraph(range(0,25)+range(75,100))
sage: K.is_isomorphic(C) and K.is_isomorphic(D)
True


The automorphism group contains only one nontrivial proper normal subgroup, which is of index 2 and is simple. It is known as the Higman-Sims group.

sage: H = graphs.HigmanSimsGraph()
sage: G = H.automorphism_group()
sage: g=G.order(); g
88704000
sage: K = G.normal_subgroups()[1]
sage: K.is_simple()
True
sage: g//K.order()
2


REFERENCES:

 [BROUWER-HS-2009] Higman-Sims graph. Andries E. Brouwer, accessed 24 October 2009.
 [HIGMAN1968] A simple group of order 44,352,000, Math.Z. 105 (1968) 110-113. D.G. Higman & C. Sims.
 [HAFNER2004] (1, 2) On the graphs of Hoffman-Singleton and Higman-Sims. The Electronic Journal of Combinatorics 11 (2004), #R77, Paul R. Hafner, accessed 24 October 2009.

AUTHOR:

• Rob Beezer (2009-10-24)
static HoffmanGraph()

Returns the Hoffman Graph.

EXAMPLES:

sage: g = graphs.HoffmanGraph()
sage: g.is_bipartite()
True
sage: g.is_hamiltonian() # long time
True
3
sage: g.diameter()
4
sage: g.automorphism_group().cardinality()
48

static HoffmanSingletonGraph()

Returns the Hoffman-Singleton graph.

The Hoffman-Singleton graph is the Moore graph of degree 7, diameter 2 and girth 5. The Hoffman-Singleton theorem states that any Moore graph with girth 5 must have degree 2, 3, 7 or 57. The first three respectively are the pentagon, the Petersen graph, and the Hoffman-Singleton graph. The existence of a Moore graph with girth 5 and degree 57 is still open.

A Moore graph is a graph with diameter $$d$$ and girth $$2d + 1$$. This implies that the graph is regular, and distance regular.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. A novel algorithm written by Tom Boothby gives a random layout which is pleasing to the eye.

REFERENCES:

 [GodsilRoyle] Godsil, C. and Royle, G. Algebraic Graph Theory. Springer, 2001.

EXAMPLES:

sage: HS = graphs.HoffmanSingletonGraph()
sage: Set(HS.degree())
{7}
sage: HS.girth()
5
sage: HS.diameter()
2
sage: HS.num_verts()
50


Note that you get a different layout each time you create the graph.

sage: HS.layout()[1]
(-0.844..., 0.535...)
sage: graphs.HoffmanSingletonGraph().layout()[1]
(-0.904..., 0.425...)

static HoltGraph()

Returns the Holt graph (also called the Doyle graph)

EXAMPLES:

sage: g = graphs.HoltGraph();g
Holt graph: Graph on 27 vertices
sage: g.is_regular()
True
sage: g.is_vertex_transitive()
True
sage: g.chromatic_number()
3
sage: g.is_hamiltonian() # long time
True
3
sage: g.diameter()
3
sage: g.girth()
5
sage: g.automorphism_group().cardinality()
54

static HortonGraph()

Returns the Horton Graph.

The Horton graph is a cubic 3-connected non-hamiltonian graph. For more information, see the Wikipedia article Horton_graph.

EXAMPLES:

sage: g = graphs.HortonGraph()
sage: g.order()
96
sage: g.size()
144
10
sage: g.diameter()
10
sage: g.girth()
6
sage: g.automorphism_group().cardinality()
96
sage: g.chromatic_number()
2
sage: g.is_hamiltonian() # not tested -- veeeery long
False

static HouseGraph()

Returns a house graph with 5 nodes.

A house graph is named for its shape. It is a triangle (roof) over a square (walls).

This constructor depends on NetworkX numeric labeling.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, the house graph is drawn with the first node in the lower-left corner of the house, the second in the lower-right corner of the house. The third node is in the upper-left corner connecting the roof to the wall, and the fourth is in the upper-right corner connecting the roof to the wall. The fifth node is the top of the roof, connected only to the third and fourth.

EXAMPLES: Construct and show a house graph

sage: g = graphs.HouseGraph()
sage: g.show() # long time

static HouseXGraph()

Returns a house X graph with 5 nodes.

A house X graph is a house graph with two additional edges. The upper-right corner is connected to the lower-left. And the upper-left corner is connected to the lower-right.

This constructor depends on NetworkX numeric labeling.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, the house X graph is drawn with the first node in the lower-left corner of the house, the second in the lower-right corner of the house. The third node is in the upper-left corner connecting the roof to the wall, and the fourth is in the upper-right corner connecting the roof to the wall. The fifth node is the top of the roof, connected only to the third and fourth.

EXAMPLES: Construct and show a house X graph

sage: g = graphs.HouseXGraph()
sage: g.show() # long time

static HyperStarGraph(n, k)

Returns the hyper-star graph HS(n,k).

The vertices of the hyper-star graph are the set of binary strings of length n which contain k 1s. Two vertices, u and v, are adjacent only if u can be obtained from v by swapping the first bit with a different symbol in another position.

INPUT:

• n
• k

EXAMPLES:

sage: g = graphs.HyperStarGraph(6,3)
sage: g.plot() # long time


REFERENCES:

• Lee, Hyeong-Ok, Jong-Seok Kim, Eunseuk Oh, and Hyeong-Seok Lim. “Hyper-Star Graph: A New Interconnection Network Improving the Network Cost of the Hypercube.” In Proceedings of the First EurAsian Conference on Information and Communication Technology, 858-865. Springer-Verlag, 2002.

AUTHORS:

• Michael Yurko (2009-09-01)
static IcosahedralGraph()

Returns an Icosahedral graph (with 12 nodes).

The regular icosahedron is a 20-sided triangular polyhedron. The icosahedral graph corresponds to the connectivity of the vertices of the icosahedron. It is dual to the dodecahedral graph. The icosahedron is symmetric, so the spring-layout algorithm will be very effective for display.

PLOTTING: The Icosahedral graph should be viewed in 3 dimensions. We chose to use the default spring-layout algorithm here, so that multiple iterations might yield a different point of reference for the user. We hope to add rotatable, 3-dimensional viewing in the future. In such a case, a string argument will be added to select the flat spring-layout over a future implementation.

EXAMPLES: Construct and show an Octahedral graph

sage: g = graphs.IcosahedralGraph()
sage: g.show() # long time


Create several icosahedral graphs in a Sage graphics array. They will be drawn differently due to the use of the spring-layout algorithm.

sage: g = []
sage: j = []
sage: for i in range(9):
....:     k = graphs.IcosahedralGraph()
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time

static IntervalGraph(intervals, points_ordered=False)

Returns the graph corresponding to the given intervals.

An interval graph is built from a list $$(a_i,b_i)_{1\leq i \leq n}$$ of intervals : to each interval of the list is associated one vertex, two vertices being adjacent if the two corresponding (closed) intervals intersect.

INPUT:

• intervals – the list of pairs $$(a_i,b_i)$$ defining the graph.
• points_ordered – states whether every interval $$(a_i,b_i)$$ of $$intervals$$ satisfies $$a_i<b_i$$. If satisfied then setting points_ordered to True will speed up the creation of the graph.

Note

• The vertices are named 0, 1, 2, and so on. The intervals used to create the graph are saved with the graph and can be recovered using get_vertex() or get_vertices().

EXAMPLE:

The following line creates the sequence of intervals $$(i, i+2)$$ for i in $$[0, ..., 8]$$:

sage: intervals = [(i,i+2) for i in range(9)]


In the corresponding graph

sage: g = graphs.IntervalGraph(intervals)
sage: g.get_vertex(3)
(3, 5)
sage: neigh = g.neighbors(3)
sage: for v in neigh: print g.get_vertex(v)
(1, 3)
(2, 4)
(4, 6)
(5, 7)


The is_interval() method verifies that this graph is an interval graph.

sage: g.is_interval()
True


The intervals in the list need not be distinct.

sage: intervals = [ (1,2), (1,2), (1,2), (2,3), (3,4) ]
sage: g = graphs.IntervalGraph(intervals,True)
sage: g.clique_maximum()
[0, 1, 2, 3]
sage: g.get_vertices()
{0: (1, 2), 1: (1, 2), 2: (1, 2), 3: (2, 3), 4: (3, 4)}


The endpoints of the intervals are not ordered we get the same graph (except for the vertex labels).

sage: rev_intervals = [ (2,1), (2,1), (2,1), (3,2), (4,3) ]
sage: h = graphs.IntervalGraph(rev_intervals,False)
sage: h.get_vertices()
{0: (2, 1), 1: (2, 1), 2: (2, 1), 3: (3, 2), 4: (4, 3)}
sage: g.edges() == h.edges()
True

static JohnsonGraph(n, k)

Returns the Johnson graph with parameters $$n, k$$.

Johnson graphs are a special class of undirected graphs defined from systems of sets. The vertices of the Johnson graph $$J(n,k)$$ are the $$k$$-element subsets of an $$n$$-element set; two vertices are adjacent when they meet in a $$(k-1)$$-element set. For more information about Johnson graphs, see the corresponding Wikipedia page.

EXAMPLES:

The Johnson graph is a Hamiltonian graph.

sage: g = graphs.JohnsonGraph(7, 3)
sage: g.is_hamiltonian()
True


Every Johnson graph is vertex transitive.

sage: g = graphs.JohnsonGraph(6, 4)
sage: g.is_vertex_transitive()
True


The complement of the Johnson graph $$J(n,2)$$ is isomorphic to the Knesser Graph $$K(n,2)$$. In paritcular the complement of $$J(5,2)$$ is isomorphic to the Petersen graph.

sage: g = graphs.JohnsonGraph(5,2)
sage: g.complement().is_isomorphic(graphs.PetersenGraph())
True


Returns the $$d$$-dimensional King Graph with prescribed dimensions.

The 2-dimensional King Graph of parameters $$n$$ and $$m$$ is a graph with $$nm$$ vertices in which each vertex represents a square in an $$n \times m$$ chessboard, and each edge corresponds to a legal move by a king.

The d-dimensional King Graph with $$d >= 2$$ has for vertex set the cells of a d-dimensional grid with prescribed dimensions, and each edge corresponds to a legal move by a king in either one or two dimensions.

All 2-dimensional King Graphs are Hamiltonian, biconnected, and have chromatic number 4 as soon as both dimensions are larger or equal to 2.

INPUTS:

• dim_list – an iterable object (list, set, dict) providing the dimensions $$n_1, n_2, \ldots, n_d$$, with $$n_i \geq 1$$, of the chessboard.
• radius – (default: None) by setting the radius to a positive integer, one may increase the power of the king to at least radius steps. When the radius equals the higher size of the dimensions, the resulting graph is a Queen Graph.
• relabel – (default: False) a boolean set to True if vertices must be relabeled as integers.

EXAMPLES:

The $$(2,2)$$-King Graph is isomorphic to the complete graph on 4 vertices:

sage: G = graphs.QueenGraph( [2, 2] )
sage: G.is_isomorphic( graphs.CompleteGraph(4) )
True


The King Graph with large enough radius is isomorphic to a Queen Graph:

sage: G = graphs.KingGraph( [5, 4], radius=5 )
sage: H = graphs.QueenGraph( [4, 5] )
sage: G.is_isomorphic( H )
True


Also True in higher dimensions:

sage: G = graphs.KingGraph( [2, 5, 4], radius=5 )
sage: H = graphs.QueenGraph( [4, 5, 2] )
sage: G.is_isomorphic( H )
True

static KittellGraph()

Returns the Kittell Graph.

EXAMPLES:

sage: g = graphs.KittellGraph()
sage: g.order()
23
sage: g.size()
63
3
sage: g.diameter()
4
sage: g.girth()
3
sage: g.chromatic_number()
4

static KneserGraph(n, k)

Returns the Kneser Graph with parameters $$n, k$$.

The Kneser Graph with parameters $$n,k$$ is the graph whose vertices are the $$k$$-subsets of $$[0,1,\dots,n-1]$$, and such that two vertices are adjacent if their corresponding sets are disjoint.

For example, the Petersen Graph can be defined as the Kneser Graph with parameters $$5,2$$.

EXAMPLE:

sage: KG=graphs.KneserGraph(5,2)
sage: print KG.vertices()
[{4, 5}, {1, 3}, {2, 5}, {2, 3}, {3, 4}, {3, 5}, {1, 4}, {1, 5}, {1, 2}, {2, 4}]
sage: P=graphs.PetersenGraph()
sage: P.is_isomorphic(KG)
True


TESTS:

sage: KG=graphs.KneserGraph(0,0)
Traceback (most recent call last):
...
ValueError: Parameter n should be a strictly positive integer
sage: KG=graphs.KneserGraph(5,6)
Traceback (most recent call last):
...
ValueError: Parameter k should be a strictly positive integer inferior to n

static KnightGraph(dim_list, one=1, two=2, relabel=False)

Returns the d-dimensional Knight Graph with prescribed dimensions.

The 2-dimensional Knight Graph of parameters $$n$$ and $$m$$ is a graph with $$nm$$ vertices in which each vertex represents a square in an $$n \times m$$ chessboard, and each edge corresponds to a legal move by a knight.

The d-dimensional Knight Graph with $$d >= 2$$ has for vertex set the cells of a d-dimensional grid with prescribed dimensions, and each edge corresponds to a legal move by a knight in any pairs of dimensions.

The $$(n,n)$$-Knight Graph is Hamiltonian for even $$n > 4$$.

INPUTS:

• dim_list – an iterable object (list, set, dict) providing the dimensions $$n_1, n_2, \ldots, n_d$$, with $$n_i \geq 1$$, of the chessboard.
• one – (default: 1) integer indicating the number on steps in one dimension.
• two – (default: 2) integer indicating the number on steps in the second dimension.
• relabel – (default: False) a boolean set to True if vertices must be relabeled as integers.

EXAMPLES:

The $$(3,3)$$-Knight Graph has an isolated vertex:

sage: G = graphs.KnightGraph( [3, 3] )
sage: G.degree( (1,1) )
0


The $$(3,3)$$-Knight Graph minus vertex (1,1) is a cycle of order 8:

sage: G = graphs.KnightGraph( [3, 3] )
sage: G.delete_vertex( (1,1) )
sage: G.is_isomorphic( graphs.CycleGraph(8) )
True


The $$(6,6)$$-Knight Graph is Hamiltonian:

sage: G = graphs.KnightGraph( [6, 6] )
sage: G.is_hamiltonian()
True

static KrackhardtKiteGraph()

Returns a Krackhardt kite graph with 10 nodes.

The Krackhardt kite graph was originally developed by David Krackhardt for the purpose of studying social networks. It is used to show the distinction between: degree centrality, betweeness centrality, and closeness centrality. For more information read the plotting section below in conjunction with the example.

REFERENCES:

This constructor depends on NetworkX numeric labeling.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, the graph is drawn left to right, in top to bottom row sequence of [2, 3, 2, 1, 1, 1] nodes on each row. This places the fourth node (3) in the center of the kite, with the highest degree. But the fourth node only connects nodes that are otherwise connected, or those in its clique (i.e.: Degree Centrality). The eighth (7) node is where the kite meets the tail. It has degree = 3, less than the average, but is the only connection between the kite and tail (i.e.: Betweenness Centrality). The sixth and seventh nodes (5 and 6) are drawn in the third row and have degree = 5. These nodes have the shortest path to all other nodes in the graph (i.e.: Closeness Centrality). Please execute the example for visualization.

EXAMPLE: Construct and show a Krackhardt kite graph

sage: g = graphs.KrackhardtKiteGraph()
sage: g.show() # long time

static LCFGraph(n, shift_list, repeats)

Returns the cubic graph specified in LCF notation.

LCF (Lederberg-Coxeter-Fruchte) notation is a concise way of describing cubic Hamiltonian graphs. The way a graph is constructed is as follows. Since there is a Hamiltonian cycle, we first create a cycle on n nodes. The variable shift_list = [s_0, s_1, ..., s_k-1] describes edges to be created by the following scheme: for each i, connect vertex i to vertex (i + s_i). Then, repeats specifies the number of times to repeat this process, where on the jth repeat we connect vertex (i + j*len(shift_list)) to vertex ( i + j*len(shift_list) + s_i).

INPUT:

• n - the number of nodes.
• shift_list - a list of integer shifts mod n.
• repeats - the number of times to repeat the process.

EXAMPLES:

sage: G = graphs.LCFGraph(4, [2,-2], 2)
sage: G.is_isomorphic(graphs.TetrahedralGraph())
True

sage: G = graphs.LCFGraph(20, [10,7,4,-4,-7,10,-4,7,-7,4], 2)
sage: G.is_isomorphic(graphs.DodecahedralGraph())
True

sage: G = graphs.LCFGraph(14, [5,-5], 7)
sage: G.is_isomorphic(graphs.HeawoodGraph())
True


The largest cubic nonplanar graph of diameter three:

sage: G = graphs.LCFGraph(20, [-10,-7,-5,4,7,-10,-7,-4,5,7,-10,-7,6,-5,7,-10,-7,5,-6,7], 1)
sage: G.degree()
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
sage: G.diameter()
3
sage: G.show()  # long time


PLOTTING: LCF Graphs are plotted as an n-cycle with edges in the middle, as described above.

REFERENCES:

• [1] Frucht, R. “A Canonical Representation of Trivalent Hamiltonian Graphs.” J. Graph Th. 1, 45-60, 1976.
• [2] Grunbaum, B. Convex Polytope es. New York: Wiley, pp. 362-364, 1967.
• [3] Lederberg, J. ‘DENDRAL-64: A System for Computer Construction, Enumeration and Notation of Organic Molecules as Tree Structures and Cyclic Graphs. Part II. Topology of Cyclic Graphs.’ Interim Report to the National Aeronautics and Space Administration. Grant NsG 81-60. December 15, 1965. http://profiles.nlm.nih.gov/BB/A/B/I/U/_/bbabiu.pdf.

Returns a ladder graph with 2*n nodes.

A ladder graph is a basic structure that is typically displayed as a ladder, i.e.: two parallel path graphs connected at each corresponding node pair.

This constructor depends on NetworkX numeric labels.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, each ladder graph will be displayed horizontally, with the first n nodes displayed left to right on the top horizontal line.

EXAMPLES: Construct and show a ladder graph with 14 nodes

sage: g = graphs.LadderGraph(7)
sage: g.show() # long time


Create several ladder graphs in a Sage graphics array

sage: g = []
sage: j = []
sage: for i in range(9):
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time

static LjubljanaGraph(embedding=1)

Returns the Ljubljana Graph.

The Ljubljana graph is a bipartite 3-regular graph on 112 vertices and 168 edges. It is not vertex-transitive as it has two orbits which are also independent sets of size 56. See the Wikipedia page on the Ljubljana Graph.

The default embedding is obtained from the Heawood graph.

INPUT:

• embedding – two embeddings are available, and can be selected by setting embedding to 1 or 2.

EXAMPLES:

sage: g = graphs.LjubljanaGraph()
sage: g.order()
112
sage: g.size()
168
sage: g.girth()
10
sage: g.diameter()
8
sage: g.show(figsize=[10, 10])   # long time
sage: graphs.LjubljanaGraph(embedding=2).show(figsize=[10, 10])   # long time


TESTS:

sage: graphs.LjubljanaGraph(embedding=3)
Traceback (most recent call last):
...
ValueError: The value of embedding must be 1 or 2.

static LollipopGraph(n1, n2)

Returns a lollipop graph with n1+n2 nodes.

A lollipop graph is a path graph (order n2) connected to a complete graph (order n1). (A barbell graph minus one of the bells).

This constructor depends on NetworkX numeric labels.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, the complete graph will be drawn in the lower-left corner with the (n1)th node at a 45 degree angle above the right horizontal center of the complete graph, leading directly into the path graph.

EXAMPLES: Construct and show a lollipop graph Candy = 13, Stick = 4

sage: g = graphs.LollipopGraph(13,4)
sage: g.show() # long time


Create several lollipop graphs in a Sage graphics array

sage: g = []
sage: j = []
sage: for i in range(6):
....:     k = graphs.LollipopGraph(i+3,4)
....:     g.append(k)
sage: for i in range(2):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time

static M22Graph()

Returns the M22 graph.

The $$M_{22}$$ graph is the unique strongly regular graph with parameters $$v = 77, k = 16, \lambda = 0, \mu = 4$$.

For more information on the $$M_{22}$$ graph, see http://www.win.tue.nl/~aeb/graphs/M22.html.

EXAMPLES:

sage: g = graphs.M22Graph()
sage: g.order()
77
sage: g.size()
616
sage: g.is_strongly_regular(parameters = True)
(77, 16, 0, 4)

static MarkstroemGraph()

Returns the Markström Graph.

The Markström Graph is a cubic planar graph with no cycles of length 4 nor 8, but containing cycles of length 16. For more information, see the Wolfram page about the Markström Graph.

EXAMPLES:

sage: g = graphs.MarkstroemGraph()
sage: g.order()
24
sage: g.size()
36
sage: g.is_planar()
True
sage: g.is_regular(3)
True
sage: g.subgraph_search(graphs.CycleGraph(4)) is None
True
sage: g.subgraph_search(graphs.CycleGraph(8)) is None
True
sage: g.subgraph_search(graphs.CycleGraph(16))
Subgraph of (Markstroem Graph): Graph on 16 vertices

static McGeeGraph(embedding=2)

Returns the McGee Graph.

INPUT:

• embedding – two embeddings are available, and can be selected by setting embedding to 1 or 2.

EXAMPLES:

sage: g = graphs.McGeeGraph()
sage: g.order()
24
sage: g.size()
36
sage: g.girth()
7
sage: g.diameter()
4
sage: g.show()
sage: graphs.McGeeGraph(embedding=1).show()


TESTS:

sage: graphs.McGeeGraph(embedding=3)
Traceback (most recent call last):
...
ValueError: The value of embedding must be 1 or 2.

static McLaughlinGraph()

Returns the McLaughlin Graph.

The McLaughlin Graph is the unique strongly regular graph of parameters $$(275, 112, 30, 56)$$.

For more information on the McLaughlin Graph, see its web page on Andries Brouwer’s website which gives the definition that this method implements.

Note

To create this graph you must have the gap_packages spkg installed.

EXAMPLES:

sage: g = graphs.McLaughlinGraph()           # optional gap_packages
sage: g.is_strongly_regular(parameters=True) # optional gap_packages
(275, 112, 30, 56)
sage: set(g.spectrum()) == {112, 2, -28}     # optional gap_packages
True

static MeredithGraph()

Returns the Meredith Graph

The Meredith Graph is a 4-regular 4-connected non-hamiltonian graph. For more information on the Meredith Graph, see the Wikipedia article Meredith_graph.

EXAMPLES:

sage: g = graphs.MeredithGraph()
sage: g.is_regular(4)
True
sage: g.order()
70
sage: g.size()
140
7
sage: g.diameter()
8
sage: g.girth()
4
sage: g.chromatic_number()
3
sage: g.is_hamiltonian() # long time
False

static MoebiusKantorGraph()

Returns a Moebius-Kantor Graph.

A Moebius-Kantor graph is a cubic symmetric graph. (See also the Heawood graph). It has 16 nodes and 24 edges. It is nonplanar and Hamiltonian. It has diameter = 4, girth = 6, and chromatic number = 2. It is identical to the Generalized Petersen graph, P[8,3].

PLOTTING: See the plotting section for the generalized Petersen graphs.

REFERENCES:

EXAMPLES:

sage: MK = graphs.MoebiusKantorGraph()
sage: MK
Moebius-Kantor Graph: Graph on 16 vertices
sage: MK.graph6_string()
'OhCGKE?O@?ACAC@I?Q_AS'
sage: (graphs.MoebiusKantorGraph()).show() # long time

static MoserSpindle()

Returns the Moser spindle.

EXAMPLES:

The Moser spindle is a planar graph having 7 vertices and 11 edges.

sage: G = graphs.MoserSpindle(); G
Moser spindle: Graph on 7 vertices
sage: G.is_planar()
True
sage: G.order()
7
sage: G.size()
11


It is a Hamiltonian graph with radius 2, diameter 2, and girth 3.

sage: G.is_hamiltonian()
True
2
sage: G.diameter()
2
sage: G.girth()
3


The Moser spindle has chromatic number 4 and its automorphism group is isomorphic to the dihedral group $$D_4$$.

sage: G.chromatic_number()
4
sage: ag = G.automorphism_group()
sage: ag.is_isomorphic(DihedralGroup(4))
True

static MycielskiGraph(k=1, relabel=True)

Returns the $$k$$-th Mycielski Graph.

The graph $$M_k$$ is triangle-free and has chromatic number equal to $$k$$. These graphs show, constructively, that there are triangle-free graphs with arbitrarily high chromatic number.

The Mycielski graphs are built recursively starting with $$M_0$$, an empty graph; $$M_1$$, a single vertex graph; and $$M_2$$ is the graph $$K_2$$. $$M_{k+1}$$ is then built from $$M_k$$ as follows:

If the vertices of $$M_k$$ are $$v_1,\ldots,v_n$$, then the vertices of $$M_{k+1}$$ are $$v_1,\ldots,v_n,w_1,\ldots,w_n,z$$. Vertices $$v_1,\ldots,v_n$$ induce a copy of $$M_k$$. Vertices $$w_1,\ldots,w_n$$ are an independent set. Vertex $$z$$ is adjacent to all the $$w_i$$-vertices. Finally, vertex $$w_i$$ is adjacent to vertex $$v_j$$ iff $$v_i$$ is adjacent to $$v_j$$.

INPUT:

• k Number of steps in the construction process.
• relabel Relabel the vertices so their names are the integers range(n) where n is the number of vertices in the graph.

EXAMPLE:

The Mycielski graph $$M_k$$ is triangle-free and has chromatic number equal to $$k$$.

sage: g = graphs.MycielskiGraph(5)
sage: g.is_triangle_free()
True
sage: g.chromatic_number()
5


The graphs $$M_4$$ is (isomorphic to) the Grotzsch graph.

sage: g = graphs.MycielskiGraph(4)
sage: g.is_isomorphic(graphs.GrotzschGraph())
True


REFERENCES:

static MycielskiStep(g)

Perform one iteration of the Mycielski construction.

See the documentation for MycielskiGraph which uses this method. We expose it to all users in case they may find it useful.

EXAMPLE. One iteration of the Mycielski step applied to the 5-cycle yields a graph isomorphic to the Grotzsch graph

sage: g = graphs.CycleGraph(5)
sage: h = graphs.MycielskiStep(g)
sage: h.is_isomorphic(graphs.GrotzschGraph())
True

static NKStarGraph(n, k)

Returns the (n,k)-star graph.

The vertices of the (n,k)-star graph are the set of all arrangements of n symbols into labels of length k. There are two adjacency rules for the (n,k)-star graph. First, two vertices are adjacent if one can be obtained from the other by swapping the first symbol with another symbol. Second, two vertices are adjacent if one can be obtained from the other by swapping the first symbol with an external symbol (a symbol not used in the original label).

INPUT:

• n
• k

EXAMPLES:

sage: g = graphs.NKStarGraph(4,2)
sage: g.plot() # long time


REFERENCES:

• Wei-Kuo, Chiang, and Chen Rong-Jaye. “The (n, k)-star graph: A generalized star graph.” Information Processing Letters 56, no. 5 (December 8, 1995): 259-264.

AUTHORS:

• Michael Yurko (2009-09-01)
static NStarGraph(n)

Returns the n-star graph.

The vertices of the n-star graph are the set of permutations on n symbols. There is an edge between two vertices if their labels differ only in the first and one other position.

INPUT:

• n

EXAMPLES:

sage: g = graphs.NStarGraph(4)
sage: g.plot() # long time


REFERENCES:

• S.B. Akers, D. Horel and B. Krishnamurthy, The star graph: An attractive alternative to the previous n-cube. In: Proc. Internat. Conf. on Parallel Processing (1987), pp. 393–400.

AUTHORS:

• Michael Yurko (2009-09-01)
static NauruGraph(embedding=2)

Returns the Nauru Graph.

INPUT:

• embedding – two embeddings are available, and can be selected by setting embedding to 1 or 2.

EXAMPLES:

sage: g = graphs.NauruGraph()
sage: g.order()
24
sage: g.size()
36
sage: g.girth()
6
sage: g.diameter()
4
sage: g.show()
sage: graphs.NauruGraph(embedding=1).show()


TESTS:

sage: graphs.NauruGraph(embedding=3)
Traceback (most recent call last):
...
ValueError: The value of embedding must be 1 or 2.
sage: graphs.NauruGraph(embedding=1).is_isomorphic(g)
True

static OctahedralGraph()

Returns an Octahedral graph (with 6 nodes).

The regular octahedron is an 8-sided polyhedron with triangular faces. The octahedral graph corresponds to the connectivity of the vertices of the octahedron. It is the line graph of the tetrahedral graph. The octahedral is symmetric, so the spring-layout algorithm will be very effective for display.

PLOTTING: The Octahedral graph should be viewed in 3 dimensions. We chose to use the default spring-layout algorithm here, so that multiple iterations might yield a different point of reference for the user. We hope to add rotatable, 3-dimensional viewing in the future. In such a case, a string argument will be added to select the flat spring-layout over a future implementation.

EXAMPLES: Construct and show an Octahedral graph

sage: g = graphs.OctahedralGraph()
sage: g.show() # long time


Create several octahedral graphs in a Sage graphics array They will be drawn differently due to the use of the spring-layout algorithm

sage: g = []
sage: j = []
sage: for i in range(9):
....:     k = graphs.OctahedralGraph()
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time

static OddGraph(n)

Returns the Odd Graph with parameter $$n$$.

The Odd Graph with parameter $$n$$ is defined as the Kneser Graph with parameters $$2n-1,n-1$$. Equivalently, the Odd Graph is the graph whose vertices are the $$n-1$$-subsets of $$[0,1,\dots,2(n-1)]$$, and such that two vertices are adjacent if their corresponding sets are disjoint.

For example, the Petersen Graph can be defined as the Odd Graph with parameter $$3$$.

EXAMPLE:

sage: OG=graphs.OddGraph(3)
sage: print OG.vertices()
[{4, 5}, {1, 3}, {2, 5}, {2, 3}, {3, 4}, {3, 5}, {1, 4}, {1, 5}, {1, 2}, {2, 4}]
sage: P=graphs.PetersenGraph()
sage: P.is_isomorphic(OG)
True


TESTS:

sage: KG=graphs.OddGraph(1)
Traceback (most recent call last):
...
ValueError: Parameter n should be an integer strictly greater than 1

static PaleyGraph(q)

Paley graph with $$q$$ vertices

Parameter $$q$$ must be the power of a prime number and congruent to 1 mod 4.

EXAMPLES:

sage: G=graphs.PaleyGraph(9);G
Paley graph with parameter 9: Graph on 9 vertices
sage: G.is_regular()
True


A Paley graph is always self-complementary:

sage: G.complement().is_isomorphic(G)
True

static PappusGraph()

Returns the Pappus graph, a graph on 18 vertices.

The Pappus graph is cubic, symmetric, and distance-regular.

EXAMPLES:

sage: G = graphs.PappusGraph()
sage: G.show()  # long time
sage: L = graphs.LCFGraph(18, [5,7,-7,7,-7,-5], 3)
sage: L.show()  # long time
sage: G.is_isomorphic(L)
True

static PathGraph(n, pos=None)

Returns a path graph with n nodes. Pos argument takes a string which is either ‘circle’ or ‘line’, (otherwise the default is used). See the plotting section below for more detail.

A path graph is a graph where all inner nodes are connected to their two neighbors and the two end-nodes are connected to their one inner neighbors. (i.e.: a cycle graph without the first and last node connected).

This constructor depends on NetworkX numeric labels.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, the graph may be drawn in one of two ways: The ‘line’ argument will draw the graph in a horizontal line (left to right) if there are less than 11 nodes. Otherwise the ‘line’ argument will append horizontal lines of length 10 nodes below, alternating left to right and right to left. The ‘circle’ argument will cause the graph to be drawn in a cycle-shape, with the first node at the top and then about the circle in a clockwise manner. By default (without an appropriate string argument) the graph will be drawn as a ‘circle’ if 10 n 41 and as a ‘line’ for all other n.

EXAMPLES: Show default drawing by size: ‘line’: n 11

sage: p = graphs.PathGraph(10)
sage: p.show() # long time


‘circle’: 10 n 41

sage: q = graphs.PathGraph(25)
sage: q.show() # long time


‘line’: n 40

sage: r = graphs.PathGraph(55)
sage: r.show() # long time


Override the default drawing:

sage: s = graphs.PathGraph(5,'circle')
sage: s.show() # long time

static PermutationGraph(second_permutation, first_permutation=None)

Builds a permutation graph from one (or two) permutations.

General definition

A Permutation Graph can be encoded by a permutation $$\sigma$$ of $$1, ..., n$$. It is then built in the following way :

Take two horizontal lines in the euclidean plane, and mark points $$1, ..., n$$ from left to right on the first of them. On the second one, still from left to right, mark point in the order in which they appear in $$\sigma$$. Now, link by a segment the two points marked with 1, then link together the points marked with 2, and so on. The permutation graph defined by the permutation is the intersection graph of those segments : there exists a point in this graph for each element from $$1$$ to $$n$$, two vertices $$i, j$$ being adjacent if the segments $$i$$ and $$j$$ cross each other.

The set of edges of the resulting graph is equal to the set of inversions of the inverse of the given permutation.

INPUT:

• second_permutation – the permutation from which the graph should be built. It corresponds to the ordering of the elements on the second line (see previous definition)

• first_permutation (optional) – the ordering of the elements on the first line. This is useful when the elements have no natural ordering, for instance when they are strings, or tuples, or anything else.

When first_permutation == None (default), it is set to be equal to sorted(second_permutation), which just yields the expected ordering when the elements of the graph are integers.

EXAMPLE:

sage: p = Permutations(5).random_element()
sage: edges = graphs.PermutationGraph(p).edges(labels =False)
sage: set(edges) == set(p.inverse().inversions())
True


TESTS:

sage: graphs.PermutationGraph([1, 2, 3], [4, 5, 6])
Traceback (most recent call last):
...
ValueError: The two permutations do not contain the same set of elements ...

static PetersenGraph()

The Petersen Graph is a named graph that consists of 10 vertices and 15 edges, usually drawn as a five-point star embedded in a pentagon.

The Petersen Graph is a common counterexample. For example, it is not Hamiltonian.

PLOTTING: See the plotting section for the generalized Petersen graphs.

EXAMPLES: We compare below the Petersen graph with the default spring-layout versus a planned position dictionary of [x,y] tuples:

sage: petersen_spring = Graph({0:[1,4,5], 1:[0,2,6], 2:[1,3,7], 3:[2,4,8], 4:[0,3,9], 5:[0,7,8], 6:[1,8,9], 7:[2,5,9], 8:[3,5,6], 9:[4,6,7]})
sage: petersen_spring.show() # long time
sage: petersen_database = graphs.PetersenGraph()
sage: petersen_database.show() # long time

static PoussinGraph()

Returns the Poussin Graph.

For more information on the Poussin Graph, see its corresponding Wolfram page.

EXAMPLES:

sage: g = graphs.PoussinGraph()
sage: g.order()
15
sage: g.is_planar()
True


Returns the $$d$$-dimensional Queen Graph with prescribed dimensions.

The 2-dimensional Queen Graph of parameters $$n$$ and $$m$$ is a graph with $$nm$$ vertices in which each vertex represents a square in an $$n \times m$$ chessboard, and each edge corresponds to a legal move by a queen.

The $$d$$-dimensional Queen Graph with $$d >= 2$$ has for vertex set the cells of a $$d$$-dimensional grid with prescribed dimensions, and each edge corresponds to a legal move by a queen in either one or two dimensions.

All 2-dimensional Queen Graphs are Hamiltonian and biconnected. The chromatic number of a $$(n,n)$$-Queen Graph is at least $$n$$, and it is exactly $$n$$ when $$n\equiv 1,5 \bmod{6}$$.

INPUTS:

• dim_list – an iterable object (list, set, dict) providing the dimensions $$n_1, n_2, \ldots, n_d$$, with $$n_i \geq 1$$, of the chessboard.
• radius – (default: None) by setting the radius to a positive integer, one may reduce the visibility of the queen to at most radius steps. When radius is 1, the resulting graph is a King Graph.
• relabel – (default: False) a boolean set to True if vertices must be relabeled as integers.

EXAMPLES:

The $$(2,2)$$-Queen Graph is isomorphic to the complete graph on 4 vertices:

sage: G = graphs.QueenGraph( [2, 2] )
sage: G.is_isomorphic( graphs.CompleteGraph(4) )
True


The Queen Graph with radius 1 is isomorphic to the King Graph:

sage: G = graphs.QueenGraph( [4, 5], radius=1 )
sage: H = graphs.KingGraph( [5, 4] )
sage: G.is_isomorphic( H )
True


Also True in higher dimensions:

sage: G = graphs.QueenGraph( [3, 4, 5], radius=1 )
sage: H = graphs.KingGraph( [5, 3, 4] )
sage: G.is_isomorphic( H )
True


The Queen Graph can be obtained from the Rook Graph and the Bishop Graph:

sage: for d in xrange(3,12):   # long time
....:     for r in xrange(1,d+1):
....:         if not G.is_isomorphic(H):
....:            print "that's not good!"

static RandomBarabasiAlbert(n, m, seed=None)

Return a random graph created using the Barabasi-Albert preferential attachment model.

A graph with m vertices and no edges is initialized, and a graph of n vertices is grown by attaching new vertices each with m edges that are attached to existing vertices, preferentially with high degree.

INPUT:

• n - number of vertices in the graph
• m - number of edges to attach from each new node
• seed - for random number generator

EXAMPLES:

We show the edge list of a random graph on 6 nodes with m = 2.

sage: graphs.RandomBarabasiAlbert(6,2).edges(labels=False)
[(0, 2), (0, 3), (0, 4), (1, 2), (2, 3), (2, 4), (2, 5), (3, 5)]


We plot a random graph on 12 nodes with m = 3.

sage: ba = graphs.RandomBarabasiAlbert(12,3)
sage: ba.show()  # long time


We view many random graphs using a graphics array:

sage: g = []
sage: j = []
sage: for i in range(1,10):
....:     k = graphs.RandomBarabasiAlbert(i+3, 3)
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show()  # long time

static RandomBipartite(n1, n2, p)

Returns a bipartite graph with $$n1+n2$$ vertices such that any edge from $$[n1]$$ to $$[n2]$$ exists with probability $$p$$.

INPUT:

• n1,n2 : Cardinalities of the two sets
• p : Probability for an edge to exist

EXAMPLE:

sage: g=graphs.RandomBipartite(5,2,0.5)
sage: g.vertices()
[(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (1, 0), (1, 1)]


TESTS:

sage: g=graphs.RandomBipartite(5,-3,0.5)
Traceback (most recent call last):
...
ValueError: n1 and n2 should be integers strictly greater than 0
sage: g=graphs.RandomBipartite(5,3,1.5)
Traceback (most recent call last):
...
ValueError: Parameter p is a probability, and so should be a real value between 0 and 1


Trac ticket #12155:

sage: graphs.RandomBipartite(5,6,.2).complement()
complement(Random bipartite graph of size 5+6 with edge probability 0.200000000000000): Graph on 11 vertices

static RandomBoundedToleranceGraph(n)

Returns a random bounded tolerance graph.

The random tolerance graph is built from a random bounded tolerance representation by using the function $$ToleranceGraph$$. This representation is a list $$((l_0,r_0,t_0), (l_1,r_1,t_1), ..., (l_k,r_k,t_k))$$ where $$k = n-1$$ and $$I_i = (l_i,r_i)$$ denotes a random interval and $$t_i$$ a random positive value less then or equal to the length of the interval $$I_i$$. The width of the representation is limited to n**2 * 2**n.

Note

The tolerance representation used to create the graph can be recovered using get_vertex() or get_vertices().

INPUT:

• n – number of vertices of the random graph.

EXAMPLE:

Every (bounded) tolerance graph is perfect. Hence, the chromatic number is equal to the clique number

sage: g = graphs.RandomBoundedToleranceGraph(8)
sage: g.clique_number() == g.chromatic_number()
True

static RandomGNM(n, m, dense=False, seed=None)

Returns a graph randomly picked out of all graphs on n vertices with m edges.

INPUT:

• n - number of vertices.
• m - number of edges.
• dense - whether to use NetworkX’s dense_gnm_random_graph or gnm_random_graph

EXAMPLES: We show the edge list of a random graph on 5 nodes with 10 edges.

sage: graphs.RandomGNM(5, 10).edges(labels=False)
[(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]


We plot a random graph on 12 nodes with m = 12.

sage: gnm = graphs.RandomGNM(12, 12)
sage: gnm.show()  # long time


We view many random graphs using a graphics array:

sage: g = []
sage: j = []
sage: for i in range(9):
....:     k = graphs.RandomGNM(i+3, i^2-i)
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show()  # long time

static RandomGNP(n, p, seed=None, fast=True, method='Sage')

Returns a random graph on $$n$$ nodes. Each edge is inserted independently with probability $$p$$.

INPUTS:

• n – number of nodes of the graph
• p – probability of an edge
• seed – integer seed for random number generator (default=None).
• fast – boolean set to True (default) to use the algorithm with time complexity in $$O(n+m)$$ proposed in [BatBra2005]. It is designed for generating large sparse graphs. It is faster than other methods for LARGE instances (try it to know whether it is useful for you).
• method – By default (method='Sage'), this function uses the method implemented in sage.graphs.graph_generators_pyx.pyx. When method='networkx', this function calls the NetworkX function fast_gnp_random_graph, unless fast=False, then gnp_random_graph. Try them to know which method is the best for you. The fast parameter is not taken into account by the ‘Sage’ method so far.

REFERENCES:

 [ErdRen1959] P. Erdos and A. Renyi. On Random Graphs, Publ. Math. 6, 290 (1959).
 [Gilbert1959] E. N. Gilbert. Random Graphs, Ann. Math. Stat., 30, 1141 (1959).
 [BatBra2005] V. Batagelj and U. Brandes. Efficient generation of large random networks. Phys. Rev. E, 71, 036113, 2005.

PLOTTING: When plotting, this graph will use the default spring-layout algorithm, unless a position dictionary is specified.

EXAMPLES: We show the edge list of a random graph on 6 nodes with probability $$p = .4$$:

sage: set_random_seed(0)
sage: graphs.RandomGNP(6, .4).edges(labels=False)
[(0, 1), (0, 5), (1, 2), (2, 4), (3, 4), (3, 5), (4, 5)]


We plot a random graph on 12 nodes with probability $$p = .71$$:

sage: gnp = graphs.RandomGNP(12,.71)
sage: gnp.show() # long time


We view many random graphs using a graphics array:

sage: g = []
sage: j = []
sage: for i in range(9):
....:     k = graphs.RandomGNP(i+3,.43)
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time
sage: graphs.RandomGNP(4,1)
Complete graph: Graph on 4 vertices


TESTS:

sage: graphs.RandomGNP(50,.2,method=50)
Traceback (most recent call last):
...
ValueError: 'method' must be equal to 'networkx' or to 'Sage'.
sage: set_random_seed(0)
sage: graphs.RandomGNP(50,.2, method="Sage").size()
243
sage: graphs.RandomGNP(50,.2, method="networkx").size()
258

static RandomHolmeKim(n, m, p, seed=None)

Returns a random graph generated by the Holme and Kim algorithm for graphs with power law degree distribution and approximate average clustering.

INPUT:

• n - number of vertices.
• m - number of random edges to add for each new node.
• p - probability of adding a triangle after adding a random edge.
• seed - for the random number generator.

From the NetworkX documentation: The average clustering has a hard time getting above a certain cutoff that depends on m. This cutoff is often quite low. Note that the transitivity (fraction of triangles to possible triangles) seems to go down with network size. It is essentially the Barabasi-Albert growth model with an extra step that each random edge is followed by a chance of making an edge to one of its neighbors too (and thus a triangle). This algorithm improves on B-A in the sense that it enables a higher average clustering to be attained if desired. It seems possible to have a disconnected graph with this algorithm since the initial m nodes may not be all linked to a new node on the first iteration like the BA model.

EXAMPLE: We show the edge list of a random graph on 8 nodes with 2 random edges per node and a probability $$p = 0.5$$ of forming triangles.

sage: graphs.RandomHolmeKim(8, 2, 0.5).edges(labels=False)
[(0, 2), (0, 5), (1, 2), (1, 3), (2, 3), (2, 4), (2, 6), (2, 7),
(3, 4), (3, 6), (3, 7), (4, 5)]

sage: G = graphs.RandomHolmeKim(12, 3, .3)
sage: G.show()  # long time


REFERENCE:

 [HolmeKim2002] Holme, P. and Kim, B.J. Growing scale-free networks with tunable clustering, Phys. Rev. E (2002). vol 65, no 2, 026107.
static RandomInterval(n)

RandomInterval() is deprecated. Use RandomIntervalGraph() instead.

TEST:

sage: g = graphs.RandomInterval(8)
doctest:...: DeprecationWarning: RandomInterval() is deprecated. Use RandomIntervalGraph() instead.
See http://trac.sagemath.org/13283 for details.

static RandomIntervalGraph(n)

Returns a random interval graph.

An interval graph is built from a list $$(a_i,b_i)_{1\leq i \leq n}$$ of intervals : to each interval of the list is associated one vertex, two vertices being adjacent if the two corresponding intervals intersect.

A random interval graph of order $$n$$ is generated by picking random values for the $$(a_i,b_j)$$, each of the two coordinates being generated from the uniform distribution on the interval $$[0,1]$$.

This definitions follows [boucheron2001].

Note

The vertices are named 0, 1, 2, and so on. The intervals used to create the graph are saved with the graph and can be recovered using get_vertex() or get_vertices().

INPUT:

• n (integer) – the number of vertices in the random graph.

EXAMPLE:

As for any interval graph, the chromatic number is equal to the clique number

sage: g = graphs.RandomIntervalGraph(8)
sage: g.clique_number() == g.chromatic_number()
True


REFERENCE:

 [boucheron2001] Boucheron, S. and FERNANDEZ de la VEGA, W., On the Independence Number of Random Interval Graphs, Combinatorics, Probability and Computing v10, issue 05, Pages 385–396, Cambridge Univ Press, 2001
static RandomLobster(n, p, q, seed=None)

Returns a random lobster.

A lobster is a tree that reduces to a caterpillar when pruning all leaf vertices. A caterpillar is a tree that reduces to a path when pruning all leaf vertices (q=0).

INPUT:

• n - expected number of vertices in the backbone
• p - probability of adding an edge to the backbone
• q - probability of adding an edge (claw) to the arms
• seed - for the random number generator

EXAMPLE: We show the edge list of a random graph with 3 backbone nodes and probabilities $$p = 0.7$$ and $$q = 0.3$$:

sage: graphs.RandomLobster(3, 0.7, 0.3).edges(labels=False)
[(0, 1), (1, 2)]

sage: G = graphs.RandomLobster(9, .6, .3)
sage: G.show()  # long time

static RandomNewmanWattsStrogatz(n, k, p, seed=None)

Returns a Newman-Watts-Strogatz small world random graph on n vertices.

From the NetworkX documentation: First create a ring over n nodes. Then each node in the ring is connected with its k nearest neighbors. Then shortcuts are created by adding new edges as follows: for each edge u-v in the underlying “n-ring with k nearest neighbors”; with probability p add a new edge u-w with randomly-chosen existing node w. In contrast with watts_strogatz_graph(), no edges are removed.

INPUT:

• n - number of vertices.
• k - each vertex is connected to its k nearest neighbors
• p - the probability of adding a new edge for each edge
• seed - for the random number generator

EXAMPLE: We show the edge list of a random graph on 7 nodes with 2 “nearest neighbors” and probability $$p = 0.2$$:

sage: graphs.RandomNewmanWattsStrogatz(7, 2, 0.2).edges(labels=False)
[(0, 1), (0, 2), (0, 3), (0, 6), (1, 2), (2, 3), (2, 4), (3, 4), (3, 6), (4, 5), (5, 6)]

sage: G = graphs.RandomNewmanWattsStrogatz(12, 2, .3)
sage: G.show()  # long time


REFERENCE:

 [NWS99] Newman, M.E.J., Watts, D.J. and Strogatz, S.H. Random graph models of social networks. Proc. Nat. Acad. Sci. USA 99, 2566-2572.
static RandomRegular(d, n, seed=None)

Returns a random d-regular graph on n vertices, or returns False on failure.

Since every edge is incident to two vertices, n*d must be even.

INPUT:

• n - number of vertices
• d - degree
• seed - for the random number generator

EXAMPLE: We show the edge list of a random graph with 8 nodes each of degree 3.

sage: graphs.RandomRegular(3, 8).edges(labels=False)
[(0, 1), (0, 4), (0, 7), (1, 5), (1, 7), (2, 3), (2, 5), (2, 6), (3, 4), (3, 6), (4, 5), (6, 7)]

sage: G = graphs.RandomRegular(3, 20)
sage: if G:
....:     G.show()  # random output, long time


REFERENCES:

 [KimVu2003] Kim, Jeong Han and Vu, Van H. Generating random regular graphs. Proc. 35th ACM Symp. on Thy. of Comp. 2003, pp 213-222. ACM Press, San Diego, CA, USA. http://doi.acm.org/10.1145/780542.780576
 [StegerWormald1999] Steger, A. and Wormald, N. Generating random regular graphs quickly. Prob. and Comp. 8 (1999), pp 377-396.
static RandomShell(constructor, seed=None)

Returns a random shell graph for the constructor given.

INPUT:

• constructor - a list of 3-tuples (n,m,d), each representing a shell
• n - the number of vertices in the shell
• m - the number of edges in the shell
• d - the ratio of inter (next) shell edges to intra shell edges
• seed - for the random number generator

EXAMPLE:

sage: G = graphs.RandomShell([(10,20,0.8),(20,40,0.8)])
sage: G.edges(labels=False)
[(0, 3), (0, 7), (0, 8), (1, 2), (1, 5), (1, 8), (1, 9), (3, 6), (3, 11), (4, 6), (4, 7), (4, 8), (4, 21), (5, 8), (5, 9), (6, 9), (6, 10), (7, 8), (7, 9), (8, 18), (10, 11), (10, 13), (10, 19), (10, 22), (10, 26), (11, 18), (11, 26), (11, 28), (12, 13), (12, 14), (12, 28), (12, 29), (13, 16), (13, 21), (13, 29), (14, 18), (16, 20), (17, 18), (17, 26), (17, 28), (18, 19), (18, 22), (18, 27), (18, 28), (19, 23), (19, 25), (19, 28), (20, 22), (24, 26), (24, 27), (25, 27), (25, 29)]
sage: G.show()  # long time

static RandomToleranceGraph(n)

Returns a random tolerance graph.

The random tolerance graph is built from a random tolerance representation by using the function $$ToleranceGraph$$. This representation is a list $$((l_0,r_0,t_0), (l_1,r_1,t_1), ..., (l_k,r_k,t_k))$$ where $$k = n-1$$ and $$I_i = (l_i,r_i)$$ denotes a random interval and $$t_i$$ a random positive value. The width of the representation is limited to n**2 * 2**n.

Note

The vertices are named 0, 1, ..., n-1. The tolerance representation used to create the graph is saved with the graph and can be recovered using get_vertex() or get_vertices().

INPUT:

• n – number of vertices of the random graph.

EXAMPLE:

Every tolerance graph is perfect. Hence, the chromatic number is equal to the clique number

sage: g = graphs.RandomToleranceGraph(8)
sage: g.clique_number() == g.chromatic_number()
True


TEST:

sage: g = graphs.RandomToleranceGraph(-2) Traceback (most recent call last): ... ValueError: The number $$n$$ of vertices must be >= 0.
static RandomTree(n)

Returns a random tree on $$n$$ nodes numbered $$0$$ through $$n-1$$.

By Cayley’s theorem, there are $$n^{n-2}$$ trees with vertex set $$\{0,1,...,n-1\}$$. This constructor chooses one of these uniformly at random.

ALGORITHM:

The algoritm works by generating an $$(n-2)$$-long random sequence of numbers chosen independently and uniformly from $$\{0,1,\ldots,n-1\}$$ and then applies an inverse Prufer transformation.

INPUT:

• n - number of vertices in the tree

EXAMPLE:

sage: G = graphs.RandomTree(10)
sage: G.is_tree()
True
sage: G.show() # long


TESTS:

Ensuring that we encounter no unexpected surprise

sage: all( graphs.RandomTree(10).is_tree()
....:      for i in range(100) )
True

static RandomTreePowerlaw(n, gamma=3, tries=100, seed=None)

Returns a tree with a power law degree distribution. Returns False on failure.

From the NetworkX documentation: A trial power law degree sequence is chosen and then elements are swapped with new elements from a power law distribution until the sequence makes a tree (size = order - 1).

INPUT:

• n - number of vertices
• gamma - exponent of power law
• tries - number of attempts to adjust sequence to make a tree
• seed - for the random number generator

EXAMPLE: We show the edge list of a random graph with 10 nodes and a power law exponent of 2.

sage: graphs.RandomTreePowerlaw(10, 2).edges(labels=False)
[(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (6, 8), (6, 9)]

sage: G = graphs.RandomTreePowerlaw(15, 2)
sage: if G:
....:     G.show()  # random output, long time

static RingedTree(k, vertex_labels=True)

Return the ringed tree on k-levels.

A ringed tree of level $$k$$ is a binary tree with $$k$$ levels (counting the root as a level), in which all vertices at the same level are connected by a ring.

More precisely, in each layer of the binary tree (i.e. a layer is the set of vertices $$[2^i...2^{i+1}-1]$$) two vertices $$u,v$$ are adjacent if $$u=v+1$$ or if $$u=2^i$$ and $$v=2^{i+1}-1$$.

Ringed trees are defined in [CFHM12].

INPUT:

• k – the number of levels of the ringed tree.
• vertex_labels (boolean) – whether to label vertices as binary words (default) or as integers.

EXAMPLE:

sage: G = graphs.RingedTree(5)
sage: P = G.plot(vertex_labels=False, vertex_size=10)
sage: P.show() # long time
sage: G.vertices()
['', '0', '00', '000', '0000', '0001', '001', '0010', '0011', '01',
'010', '0100', '0101', '011', '0110', '0111', '1', '10', '100',
'1000', '1001', '101', '1010', '1011', '11', '110', '1100', '1101',
'111', '1110', '1111']


TEST:

sage: G = graphs.RingedTree(-1)
Traceback (most recent call last):
...
ValueError: The number of levels must be >= 1.
sage: G = graphs.RingedTree(5, vertex_labels = False)
sage: G.vertices()
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]


REFERENCES:

 [CFHM12] On the Hyperbolicity of Small-World and Tree-Like Random Graphs Wei Chen, Wenjie Fang, Guangda Hu, Michael W. Mahoney http://arxiv.org/abs/1201.1717
static RobertsonGraph()

Returns the Robertson graph.

EXAMPLE:

sage: g = graphs.RobertsonGraph()
sage: g.order()
19
sage: g.size()
38
sage: g.diameter()
3
sage: g.girth()
5
sage: g.charpoly().factor()
(x - 4) * (x - 1)^2 * (x^2 + x - 5) * (x^2 + x - 1) * (x^2 - 3)^2 * (x^2 + x - 4)^2 * (x^2 + x - 3)^2
sage: g.chromatic_number()
3
sage: g.is_hamiltonian()
True
sage: g.is_vertex_transitive()
False


Returns the $$d$$-dimensional Rook’s Graph with prescribed dimensions.

The 2-dimensional Rook’s Graph of parameters $$n$$ and $$m$$ is a graph with $$nm$$ vertices in which each vertex represents a square in an $$n \times m$$ chessboard, and each edge corresponds to a legal move by a rook.

The $$d$$-dimensional Rook Graph with $$d >= 2$$ has for vertex set the cells of a $$d$$-dimensional grid with prescribed dimensions, and each edge corresponds to a legal move by a rook in any of the dimensions.

The Rook’s Graph for an $$n\times m$$ chessboard may also be defined as the Cartesian product of two complete graphs $$K_n \square K_m$$.

INPUTS:

• dim_list – an iterable object (list, set, dict) providing the dimensions $$n_1, n_2, \ldots, n_d$$, with $$n_i \geq 1$$, of the chessboard.
• radius – (default: None) by setting the radius to a positive integer, one may decrease the power of the rook to at most radius steps. When the radius is 1, the resulting graph is a d-dimensional grid.
• relabel – (default: False) a boolean set to True if vertices must be relabeled as integers.

EXAMPLES:

The $$(n,m)$$-Rook’s Graph is isomorphic to the cartesian product of two complete graphs:

sage: G = graphs.RookGraph( [3, 4] )
sage: H = ( graphs.CompleteGraph(3) ).cartesian_product( graphs.CompleteGraph(4) )
sage: G.is_isomorphic( H )
True


When the radius is 1, the Rook’s Graph is a grid:

sage: G = graphs.RookGraph( [3, 3, 4], radius=1 )
sage: H = graphs.GridGraph( [3, 4, 3] )
sage: G.is_isomorphic( H )
True

static SchlaefliGraph()

Returns the Schläfli graph.

The Schläfli graph is the only strongly regular graphs of parameters $$(27,16,10,8)$$ (see [GodsilRoyle]).

Graph.is_strongly_regular() – tests whether a graph is strongly regular and/or returns its parameters.

Todo

Find a beautiful layout for this beautiful graph.

EXAMPLE:

Checking that the method actually returns the Schläfli graph:

sage: S = graphs.SchlaefliGraph()
sage: S.is_strongly_regular(parameters = True)
(27, 16, 10, 8)


The graph is vertex-transitive:

sage: S.is_vertex_transitive()
True


The neighborhood of each vertex is isomorphic to the complement of the Clebsch graph:

sage: neighborhood = S.subgraph(vertices = S.neighbors(0))
sage: graphs.ClebschGraph().complement().is_isomorphic(neighborhood)
True

static ShrikhandeGraph()

Returns the Shrikhande graph.

For more information, see the MathWorld article on the Shrikhande graph or the Wikipedia article on the Shrikhande graph.

Graph.is_strongly_regular() – tests whether a graph is strongly regular and/or returns its parameters.

EXAMPLES:

The Shrikhande graph was defined by S. S. Shrikhande in 1959. It has $$16$$ vertices and $$48$$ edges, and is strongly regular of degree $$6$$ with parameters $$(2,2)$$:

sage: G = graphs.ShrikhandeGraph(); G
Shrikhande graph: Graph on 16 vertices
sage: G.order()
16
sage: G.size()
48
sage: G.is_regular(6)
True
sage: set([ len([x for x in G.neighbors(i) if x in G.neighbors(j)])
....:     for i in range(G.order())
....:     for j in range(i) ])
set([2])


It is non-planar, and both Hamiltonian and Eulerian:

sage: G.is_planar()
False
sage: G.is_hamiltonian()
True
sage: G.is_eulerian()
True


It has radius $$2$$, diameter $$2$$, and girth $$3$$:

sage: G.radius()
2
sage: G.diameter()
2
sage: G.girth()
3


Its chromatic number is $$4$$ and its automorphism group is of order $$192$$:

sage: G.chromatic_number()
4
sage: G.automorphism_group().cardinality()
192


It is an integral graph since it has only integral eigenvalues:

sage: G.characteristic_polynomial().factor()
(x - 6) * (x - 2)^6 * (x + 2)^9


It is a toroidal graph, and its embedding on a torus is dual to an embedding of the Dyck graph (DyckGraph).

static SimsGewirtzGraph()

Returns the Sims-Gewirtz Graph.

This graph is obtained from the Higman Sims graph by considering the graph induced by the vertices at distance two from the vertices of an (any) edge. It is the only strongly regular graph with parameters $$v = 56, k = 10, \lambda = 0, \mu = 2$$

EXAMPLE:

sage: g = graphs.SimsGewirtzGraph(); g
Sims-Gewirtz Graph: Graph on 56 vertices
sage: g.order()
56
sage: g.size()
280
sage: g.is_strongly_regular(parameters = True)
(56, 10, 0, 2)

static SousselierGraph()

Returns the Sousselier Graph.

The Sousselier graph is a hypohamiltonian graph on 16 vertices and 27 edges. For more information, see the corresponding Wikipedia page (in French).

EXAMPLES:

sage: g = graphs.SousselierGraph()
sage: g.order()
16
sage: g.size()
27
2
sage: g.diameter()
3
sage: g.automorphism_group().cardinality()
2
sage: g.is_hamiltonian()
False
sage: g.delete_vertex(g.random_vertex())
sage: g.is_hamiltonian()
True

static StarGraph(n)

Returns a star graph with n+1 nodes.

A Star graph is a basic structure where one node is connected to all other nodes.

This constructor is dependent on NetworkX numeric labels.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, each star graph will be displayed with the first (0) node in the center, the second node (1) at the top, with the rest following in a counterclockwise manner. (0) is the node connected to all other nodes.

The star graph is a good opportunity to compare efficiency of filling a position dictionary vs. using the spring-layout algorithm for plotting. As far as display, the spring-layout should push all other nodes away from the (0) node, and thus look very similar to this constructor’s positioning.

EXAMPLES:

sage: import networkx


Compare the plots:

sage: n = networkx.star_graph(23)
sage: spring23 = Graph(n)
sage: posdict23 = graphs.StarGraph(23)
sage: spring23.show() # long time
sage: posdict23.show() # long time


View many star graphs as a Sage Graphics Array

With this constructor (i.e., the position dictionary filled)

sage: g = []
sage: j = []
sage: for i in range(9):
....:     k = graphs.StarGraph(i+3)
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time


Compared to plotting with the spring-layout algorithm

sage: g = []
sage: j = []
sage: for i in range(9):
....:     spr = networkx.star_graph(i+3)
....:     k = Graph(spr)
....:     g.append(k)
sage: for i in range(3):
....:     n = []
....:     for m in range(3):
....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time

static SylvesterGraph()

Returns the Sylvester Graph.

This graph is obtained from the Hoffman Singleton graph by considering the graph induced by the vertices at distance two from the vertices of an (any) edge.

EXAMPLE:

sage: g = graphs.SylvesterGraph(); g
Sylvester Graph: Graph on 36 vertices
sage: g.order()
36
sage: g.size()
90
sage: g.is_regular(k=5)
True

static SymplecticGraph(d, q)

Returns the Symplectic graph $$Sp(d,q)$$

The Symplectic Graph $$Sp(d,q)$$ is built from a projective space of dimension $$d-1$$ over a field $$F_q$$, and a symplectic form $$f$$. Two vertices $$u,v$$ are made adjacent if $$f(u,v)=0$$.

INPUT:

• d,q (integers) – note that only even values of $$d$$ are accepted by the function.

EXAMPLES:

sage: g = graphs.SymplecticGraph(6,2)
sage: g.is_strongly_regular(parameters=True)
(63, 30, 13, 15)
sage: set(g.spectrum()) == {-5, 3, 30}
True

static SzekeresSnarkGraph()

Returns the Szekeres Snark Graph.

The Szekeres graph is a snark with 50 vertices and 75 edges. For more information on this graph, see the Wikipedia article Szekeres_snark.

EXAMPLES:

sage: g = graphs.SzekeresSnarkGraph()
sage: g.order()
50
sage: g.size()
75
sage: g.chromatic_number()
3

static TetrahedralGraph()

Returns a tetrahedral graph (with 4 nodes).

A tetrahedron is a 4-sided triangular pyramid. The tetrahedral graph corresponds to the connectivity of the vertices of the tetrahedron. This graph is equivalent to a wheel graph with 4 nodes and also a complete graph on four nodes. (See examples below).

PLOTTING: The tetrahedral graph should be viewed in 3 dimensions. We chose to use the default spring-layout algorithm here, so that multiple iterations might yield a different point of reference for the user. We hope to add rotatable, 3-dimensional viewing in the future. In such a case, a string argument will be added to select the flat spring-layout over a future implementation.

EXAMPLES: Construct and show a Tetrahedral graph

sage: g = graphs.TetrahedralGraph()
sage: g.show() # long time


The following example requires networkx:

sage: import networkx as NX


Compare this Tetrahedral, Wheel(4), Complete(4), and the Tetrahedral plotted with the spring-layout algorithm below in a Sage graphics array:

sage: tetra_pos = graphs.TetrahedralGraph()
sage: tetra_spring = Graph(NX.tetrahedral_graph())
sage: wheel = graphs.WheelGraph(4)
sage: complete = graphs.CompleteGraph(4)
sage: g = [tetra_pos, tetra_spring, wheel, complete]
sage: j = []
sage: for i in range(2):
....:     n = []
....:     for m in range(2):
....:         n.append(g[i + m].plot(vertex_size=50, vertex_labels=False))
....:     j.append(n)
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time

static ThomsenGraph()

Returns the Thomsen Graph.

The Thomsen Graph is actually a complete bipartite graph with $$(n1, n2) = (3, 3)$$. It is also called the Utility graph.

PLOTTING: See CompleteBipartiteGraph.

EXAMPLES:

sage: T = graphs.ThomsenGraph()
sage: T
Thomsen graph: Graph on 6 vertices
sage: T.graph6_string()
'EFz_'
sage: (graphs.ThomsenGraph()).show() # long time

static TietzeGraph()

Returns the Tietze Graph.

For more information on the Tietze Graph, see the Wikipedia article Tietze’s_graph.

EXAMPLES:

sage: g = graphs.TietzeGraph()
sage: g.order()
12
sage: g.size()
18
sage: g.diameter()
3
sage: g.girth()
3
sage: g.automorphism_group().cardinality()
12
sage: g.automorphism_group().is_isomorphic(groups.permutation.Dihedral(6))
True

static ToleranceGraph(tolrep)

Returns the graph generated by the tolerance representation tolrep.

The tolerance representation tolrep is described by the list $$((l_0,r_0,t_0), (l_1,r_1,t_1), ..., (l_k,r_k,t_k))$$ where $$I_i = (l_i,r_i)$$ denotes a closed interval on the real line with $$l_i < r_i$$ and $$t_i$$ a positive value, called tolerance. This representation generates the tolerance graph with the vertex set {0,1, ..., k} and the edge set $${(i,j): |I_i \cap I_j| \ge \min{t_i, t_j}}$$ where $$|I_i \cap I_j|$$ denotes the length of the intersection of $$I_i$$ and $$I_j$$.

INPUT:

• tolrep – list of triples $$(l_i,r_i,t_i)$$ where $$(l_i,r_i)$$ denotes a closed interval on the real line and $$t_i$$ a positive value.

Note

The vertices are named 0, 1, ..., k. The tolerance representation used to create the graph is saved with the graph and can be recovered using get_vertex() or get_vertices().

EXAMPLE:

The following code creates a tolerance representation tolrep, generates its tolerance graph g, and applies some checks:

sage: tolrep = [(1,4,3),(1,2,1),(2,3,1),(0,3,3)]
sage: g = graphs.ToleranceGraph(tolrep)
sage: g.get_vertex(3)
(0, 3, 3)
sage: neigh = g.neighbors(3)
sage: for v in neigh: print g.get_vertex(v)
(1, 2, 1)
(2, 3, 1)
sage: g.is_interval()
False
sage: g.is_weakly_chordal()
True


The intervals in the list need not be distinct

sage: tolrep2 = [(0,4,5),(1,2,1),(2,3,1),(0,4,5)]
sage: g2 = graphs.ToleranceGraph(tolrep2)
sage: g2.get_vertices()
{0: (0, 4, 5), 1: (1, 2, 1), 2: (2, 3, 1), 3: (0, 4, 5)}
sage: g2.is_isomorphic(g)
True


Real values are also allowed

sage: tolrep = [(0.1,3.3,4.4),(1.1,2.5,1.1),(1.4,4.4,3.3)]
sage: g = graphs.ToleranceGraph(tolrep)
sage: g.is_isomorphic(graphs.PathGraph(3))
True


TEST:

Giving negative third value:

sage: tolrep = [(0.1,3.3,-4.4),(1.1,2.5,1.1),(1.4,4.4,3.3)]
sage: g = graphs.ToleranceGraph(tolrep)
Traceback (most recent call last):
...
ValueError: Invalid tolerance representation at position 0; third value must be positive!

static Toroidal6RegularGrid2dGraph(n1, n2)

Returns a toroidal 6-regular grid.

The toroidal 6-regular grid is a 6-regular graph on $$n_1\times n_2$$ vertices and its elements have coordinates $$(i,j)$$ for $$i \in \{0...i-1\}$$ and $$j \in \{0...j-1\}$$.

Its edges are those of the ToroidalGrid2dGraph(), to which are added the edges between $$(i,j)$$ and $$((i+1)\%n_1, (j+1)\%n_2)$$.

INPUT:

• n1, n2 (integers) – see above.

EXAMPLE:

The toroidal 6-regular grid on $$25$$ elements:

sage: g = graphs.Toroidal6RegularGrid2dGraph(5,5)
sage: g.is_regular(k=6)
True
sage: g.is_vertex_transitive()
True
sage: g.line_graph().is_vertex_transitive()
True
sage: g.automorphism_group().cardinality()
300
sage: g.is_hamiltonian()
True


TESTS:

Senseless input:

sage: graphs.Toroidal6RegularGrid2dGraph(5,2)
Traceback (most recent call last):
...
ValueError: Parameters n1 and n2 must be integers larger than 3 !
sage: graphs.Toroidal6RegularGrid2dGraph(2,0)
Traceback (most recent call last):
...
ValueError: Parameters n1 and n2 must be integers larger than 3 !

static ToroidalGrid2dGraph(n1, n2)

Returns a toroidal 2-dimensional grid graph with $$n_1n_2$$ nodes ($$n_1$$ rows and $$n_2$$ columns).

The toroidal 2-dimensional grid with parameters $$n_1,n_2$$ is the 2-dimensional grid graph with identical parameters to which are added the edges $$((i,0),(i,n_2-1))$$ and $$((0,i),(n_1-1,i))$$.

EXAMPLE:

The toroidal 2-dimensional grid is a regular graph, while the usual 2-dimensional grid is not

sage: tgrid = graphs.ToroidalGrid2dGraph(8,9)
sage: print tgrid
Toroidal 2D Grid Graph with parameters 8,9
sage: grid = graphs.Grid2dGraph(8,9)
sage: grid.is_regular()
False
sage: tgrid.is_regular()
True

static Tutte12Cage()

Returns Tutte’s 12-Cage.

EXAMPLES:

sage: g = graphs.Tutte12Cage()
sage: g.order()
126
sage: g.size()
189
sage: g.girth()
12
sage: g.diameter()
6
sage: g.show()

static TutteCoxeterGraph(embedding=2)

Returns the Tutte-Coxeter graph.

INPUT:

• embedding – two embeddings are available, and can be selected by setting embedding to 1 or 2.

EXAMPLES:

sage: g = graphs.TutteCoxeterGraph()
sage: g.order()
30
sage: g.size()
45
sage: g.girth()
8
sage: g.diameter()
4
sage: g.show()
sage: graphs.TutteCoxeterGraph(embedding=1).show()


TESTS:

sage: graphs.TutteCoxeterGraph(embedding=3)
Traceback (most recent call last):
...
ValueError: The value of embedding must be 1 or 2.

static TutteGraph()

Returns the Tutte Graph.

The Tutte graph is a 3-regular, 3-connected, and planar non-hamiltonian graph. For more information on the Tutte Graph, see the Wikipedia article Tutte_graph.

EXAMPLES:

sage: g = graphs.TutteGraph()
sage: g.order()
46
sage: g.size()
69
sage: g.is_planar()
True
sage: g.vertex_connectivity() # long
3
sage: g.girth()
4
sage: g.automorphism_group().cardinality()
3
sage: g.is_hamiltonian()
False

static WagnerGraph()

Returns the Wagner Graph.

EXAMPLES:

sage: g = graphs.WagnerGraph()
sage: g.order()
8
sage: g.size()
12
sage: g.girth()
4
sage: g.diameter()
2
sage: g.show()

static WatkinsSnarkGraph()

Returns the Watkins Snark Graph.

The Watkins Graph is a snark with 50 vertices and 75 edges. For more information, see the Wikipedia article Watkins_snark.

EXAMPLES:

sage: g = graphs.WatkinsSnarkGraph()
sage: g.order()
50
sage: g.size()
75
sage: g.chromatic_number()
3

static WellsGraph()

Returns the Wells graph.

The implementation follows the construction given on page 266 of [BCN89]. This requires to create intermediate graphs and run a small isomorphism test, while everything could be replaced by a pre-computed list of edges : I believe that it is better to keep “the recipe” in the code, however, as it is quite unlikely that this could become the most time-consuming operation in any sensible algorithm, and .... “preserves knowledge”, which is what open-source software is meant to do.

EXAMPLES:

sage: g = graphs.WellsGraph(); g
Wells graph: Graph on 32 vertices
sage: g.order()
32
sage: g.size()
80
sage: g.girth()
5
sage: g.diameter()
4
sage: g.chromatic_number()
4
sage: g.is_regular(k=5)
True


REFERENCES:

 [BCN89] A. E. Brouwer, A. M. Cohen, A. Neumaier, Distance-Regular Graphs, Springer, 1989.
static WheelGraph(n)

Returns a Wheel graph with n nodes.

A Wheel graph is a basic structure where one node is connected to all other nodes and those (outer) nodes are connected cyclically.

This constructor depends on NetworkX numeric labels.

PLOTTING: Upon construction, the position dictionary is filled to override the spring-layout algorithm. By convention, each wheel graph will be displayed with the first (0) node in the center, the second node at the top, and the rest following in a counterclockwise manner.

With the wheel graph, we see that it doesn’t take a very large n at all for the spring-layout to give a counter-intuitive display. (See Graphics Array examples below).

EXAMPLES: We view many wheel graphs with a Sage Graphics Array, first with this constructor (i.e., the position dictionary filled):

sage: g = []
sage: j = []
sage: for i in range(9):
...    k = graphs.WheelGraph(i+3)
...    g.append(k)
...
sage: for i in range(3):
...    n = []
...    for m in range(3):
...        n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
...    j.append(n)
...
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time


Next, using the spring-layout algorithm:

sage: import networkx
sage: g = []
sage: j = []
sage: for i in range(9):
...    spr = networkx.wheel_graph(i+3)
...    k = Graph(spr)
...    g.append(k)
...
sage: for i in range(3):
...    n = []
...    for m in range(3):
...        n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
...    j.append(n)
...
sage: G = sage.plot.graphics.GraphicsArray(j)
sage: G.show() # long time


Compare the plotting:

sage: n = networkx.wheel_graph(23)
sage: spring23 = Graph(n)
sage: posdict23 = graphs.WheelGraph(23)
sage: spring23.show() # long time
sage: posdict23.show() # long time

static WienerArayaGraph()

Returns the Wiener-Araya Graph.

The Wiener-Araya Graph is a planar hypohamiltonian graph on 42 vertices and 67 edges. For more information, see the Wolfram Page on the Wiener-Araya Graph or its (french) Wikipedia page.

EXAMPLES:

sage: g = graphs.WienerArayaGraph()
sage: g.order()
42
sage: g.size()
67
sage: g.girth()
4
sage: g.is_planar()
True
sage: g.is_hamiltonian() # not tested -- around 30s long
False
sage: g.delete_vertex(g.random_vertex())
sage: g.is_hamiltonian()
True

static WorldMap()

Returns the Graph of all the countries, in which two countries are adjacent in the graph if they have a common boundary.

This graph has been built from the data available in The CIA World Factbook [CIA] (2009-08-21).

The returned graph G has a member G.gps_coordinates equal to a dictionary containing the GPS coordinates of each country’s capital city.

EXAMPLE:

sage: g=graphs.WorldMap()
sage: g.has_edge("France","Italy")
True
sage: g.gps_coordinates["Bolivia"]
[[17, 'S'], [65, 'W']]
sage: sorted(g.connected_component_containing_vertex('Ireland'))
['Ireland', 'United Kingdom']


REFERENCE:

cospectral_graphs(vertices, matrix_function=<function <lambda> at 0x5993398>, graphs=None)

Find all sets of graphs on vertices vertices (with possible restrictions) which are cospectral with respect to a constructed matrix.

INPUT:

• vertices - The number of vertices in the graphs to be tested

• matrix_function - A function taking a graph and giving back a matrix. This defaults to the adjacency matrix. The spectra examined are the spectra of these matrices.

• graphs - One of three things:

• None (default) - test all graphs having vertices vertices
• a function taking a graph and returning True or False - test only the graphs on vertices vertices for which the function returns True
• a list of graphs (or other iterable object) - these graphs are tested for cospectral sets. In this case, vertices is ignored.

OUTPUT:

A list of lists of graphs. Each sublist will be a list of cospectral graphs (lists of cadinality 1 being omitted).

Graph.is_strongly_regular() – tests whether a graph is strongly regular and/or returns its parameters.

EXAMPLES:

sage: g=graphs.cospectral_graphs(5)
sage: sorted(sorted(g.graph6_string() for g in glist) for glist in g)
[['Dr?', 'Ds_']]
sage: g[0][1].am().charpoly()==g[0][1].am().charpoly()
True


There are two sets of cospectral graphs on six vertices with no isolated vertices:

sage: g=graphs.cospectral_graphs(6, graphs=lambda x: min(x.degree())>0)
sage: sorted(sorted(g.graph6_string() for g in glist) for glist in g)
[['Ep__', 'Er?G'], ['ExGg', 'ExoG']]
sage: g[0][1].am().charpoly()==g[0][1].am().charpoly()
True
sage: g[1][1].am().charpoly()==g[1][1].am().charpoly()
True


There is one pair of cospectral trees on eight vertices:

sage: g=graphs.cospectral_graphs(6, graphs=graphs.trees(8))
sage: sorted(sorted(g.graph6_string() for g in glist) for glist in g)
[['GiPC?C', 'GiQCC?']]
sage: g[0][1].am().charpoly()==g[0][1].am().charpoly()
True


There are two sets of cospectral graphs (with respect to the Laplacian matrix) on six vertices:

sage: g=graphs.cospectral_graphs(6, matrix_function=lambda g: g.laplacian_matrix())
sage: sorted(sorted(g.graph6_string() for g in glist) for glist in g)
[['Edq_', 'ErcG'], ['Exoo', 'EzcG']]
sage: g[0][1].laplacian_matrix().charpoly()==g[0][1].laplacian_matrix().charpoly()
True
sage: g[1][1].laplacian_matrix().charpoly()==g[1][1].laplacian_matrix().charpoly()
True


To find cospectral graphs with respect to the normalized Laplacian, assuming the graphs do not have an isolated vertex, it is enough to check the spectrum of the matrix $$D^{-1}A$$, where $$D$$ is the diagonal matrix of vertex degrees, and A is the adjacency matrix. We find two such cospectral graphs (for the normalized Laplacian) on five vertices:

sage: def DinverseA(g):
...     for i in range(g.order()):
...         A.rescale_row(i, 1/len(A.nonzero_positions_in_row(i)))
...     return A
sage: g=graphs.cospectral_graphs(5, matrix_function=DinverseA, graphs=lambda g: min(g.degree())>0)
sage: sorted(sorted(g.graph6_string() for g in glist) for glist in g)
[['Dlg', 'Ds_']]
sage: g[0][1].laplacian_matrix(normalized=True).charpoly()==g[0][1].laplacian_matrix(normalized=True).charpoly()
True

fullerenes(order, ipr=False)

Returns a generator which creates fullerene graphs using the buckygen generator (see [buckygen]).

INPUT:

• order - a positive even integer smaller than or equal to 254. This specifies the number of vertices in the generated fullerenes.
• ipr - default: False - if True only fullerenes that satisfy the Isolated Pentagon Rule are generated. This means that no pentagonal faces share an edge.

OUTPUT:

A generator which will produce the fullerene graphs as Sage graphs with an embedding set. These will be simple graphs: no loops, no multiple edges, no directed edges.

EXAMPLES:

There are 1812 isomers of $$\textrm{C}_{60}$$, i.e., 1812 fullerene graphs on 60 vertices:

sage: gen = graphs.fullerenes(60)  # optional buckygen
sage: len(list(gen))  # optional buckygen
1812


However, there is only one IPR fullerene graph on 60 vertices: the famous Buckminster Fullerene:

sage: gen = graphs.fullerenes(60, ipr=True)  # optional buckygen
sage: gen.next()  # optional buckygen
Graph on 60 vertices
sage: gen.next()  # optional buckygen
Traceback (most recent call last):
...
StopIteration


The unique fullerene graph on 20 vertices is isomorphic to the dodecahedron graph.

sage: gen = graphs.fullerenes(20)  # optional buckygen
sage: g = gen.next()  # optional buckygen
sage: g.is_isomorphic(graphs.DodecahedralGraph()) # optional buckygen
True
sage: g.get_embedding()  # optional buckygen
{1: [2, 3, 4],
2: [1, 5, 6],
3: [1, 7, 8],
4: [1, 9, 10],
5: [2, 10, 11],
6: [2, 12, 7],
7: [3, 6, 13],
8: [3, 14, 9],
9: [4, 8, 15],
10: [4, 16, 5],
11: [5, 17, 12],
12: [6, 11, 18],
13: [7, 18, 14],
14: [8, 13, 19],
15: [9, 19, 16],
16: [10, 15, 17],
17: [11, 16, 20],
18: [12, 20, 13],
19: [14, 20, 15],
20: [17, 19, 18]}
sage: g.plot3d(layout='spring')  # optional buckygen


REFERENCE:

 [buckygen] G. Brinkmann, J. Goedgebeur and B.D. McKay, Generation of Fullerenes, Journal of Chemical Information and Modeling, 52(11):2910-2918, 2012.
fusenes(hexagon_count, benzenoids=False)

Returns a generator which creates fusenes and benzenoids using the benzene generator (see [benzene]). Fusenes are planar polycyclic hydrocarbons with all bounded faces hexagons. Benzenoids are fusenes that are subgraphs of the hexagonal lattice.

INPUT:

• hexagon_count - a positive integer smaller than or equal to 30. This specifies the number of hexagons in the generated benzenoids.
• benzenoids - default: False - if True only benzenoids are generated.

OUTPUT:

A generator which will produce the fusenes as Sage graphs with an embedding set. These will be simple graphs: no loops, no multiple edges, no directed edges.

EXAMPLES:

There is a unique fusene with 2 hexagons:

sage: gen = graphs.fusenes(2)  # optional benzene
sage: len(list(gen))  # optional benzene
1


This fusene is naphtalene ($$\textrm{C}_{10}\textrm{H}_{8}$$). In the fusene graph the H-atoms are not stored, so this is a graph on just 10 vertices:

sage: gen = graphs.fusenes(2)  # optional benzene
sage: gen.next()  # optional benzene
Graph on 10 vertices
sage: gen.next()  # optional benzene
Traceback (most recent call last):
...
StopIteration


There are 6505 benzenoids with 9 hexagons:

sage: gen = graphs.fusenes(9, benzenoids=True)  # optional benzene
sage: len(list(gen))  # optional benzene
6505


REFERENCE:

 [benzene] G. Brinkmann, G. Caporossi and P. Hansen, A Constructive Enumeration of Fusenes and Benzenoids, Journal of Algorithms, 45:155-166, 2002.
static line_graph_forbidden_subgraphs()

Returns the 9 forbidden subgraphs of a line graph.

Wikipedia article on the line graphs

The graphs are returned in the ordering given by the Wikipedia drawing, read from left to right and from top to bottom.

EXAMPLE:

sage: graphs.line_graph_forbidden_subgraphs()
[Claw graph: Graph on 4 vertices,
Graph on 6 vertices,
Graph on 6 vertices,
Graph on 5 vertices,
Graph on 6 vertices,
Graph on 6 vertices,
Graph on 6 vertices,
Graph on 6 vertices,
Graph on 5 vertices]

nauty_geng(options='', debug=False)

Returns a generator which creates graphs from nauty’s geng program.

Note

Due to license restrictions, the nauty package is distributed as a Sage optional package. At a system command line, execute sage -i nauty to see the nauty license and install the package.

INPUT:

• options - a string passed to geng as if it was run at a system command line. At a minimum, you must pass the number of vertices you desire. Sage expects the graphs to be in nauty’s “graph6” format, do not set an option to change this default or results will be unpredictable.
• debug - default: False - if True the first line of geng’s output to standard error is captured and the first call to the generator’s next() function will return this line as a string. A line leading with “>A” indicates a successful initiation of the program with some information on the arguments, while a line beginning with “>E” indicates an error with the input.

The possible options, obtained as output of geng --help:

     n    : the number of vertices
mine:maxe : a range for the number of edges
#:0 means '# or more' except in the case 0:0
res/mod : only generate subset res out of subsets 0..mod-1

-c    : only write connected graphs
-C    : only write biconnected graphs
-t    : only generate triangle-free graphs
-f    : only generate 4-cycle-free graphs
-b    : only generate bipartite graphs
(-t, -f and -b can be used in any combination)
-m    : save memory at the expense of time (only makes a
difference in the absence of -b, -t, -f and n <= 28).
-d#   : a lower bound for the minimum degree
-D#   : a upper bound for the maximum degree
-v    : display counts by number of edges
-l    : canonically label output graphs

-q    : suppress auxiliary output (except from -v)

Options which cause geng to use an output format different than the graph6 format are not listed above (-u, -g, -s, -y, -h) as they will confuse the creation of a Sage graph. The res/mod option can be useful when using the output in a routine run several times in parallel.

OUTPUT:

A generator which will produce the graphs as Sage graphs. These will be simple graphs: no loops, no multiple edges, no directed edges.

Graph.is_strongly_regular() – tests whether a graph is strongly regular and/or returns its parameters.

EXAMPLES:

The generator can be used to construct graphs for testing, one at a time (usually inside a loop). Or it can be used to create an entire list all at once if there is sufficient memory to contain it.

sage: gen = graphs.nauty_geng("2") # optional nauty
sage: gen.next() # optional nauty
Graph on 2 vertices
sage: gen.next() # optional nauty
Graph on 2 vertices
sage: gen.next() # optional nauty
Traceback (most recent call last):
...
StopIteration: Exhausted list of graphs from nauty geng


A list of all graphs on 7 vertices. This agrees with Sloane’s OEIS sequence A000088.

sage: gen = graphs.nauty_geng("7") # optional nauty
sage: len(list(gen))  # optional nauty
1044


A list of just the connected graphs on 7 vertices. This agrees with Sloane’s OEIS sequence A001349.

sage: gen = graphs.nauty_geng("7 -c") # optional nauty
sage: len(list(gen))  # optional nauty
853


The debug switch can be used to examine geng’s reaction to the input in the options string. We illustrate success. (A failure will be a string beginning with “>E”.) Passing the “-q” switch to geng will supress the indicator of a successful initiation.

sage: gen = graphs.nauty_geng("4", debug=True) # optional nauty
sage: print gen.next() # optional nauty
>A nauty-geng -d0D3 n=4 e=0-6

static trees(vertices)

Returns a generator of the distinct trees on a fixed number of vertices.

INPUT:

• vertices - the size of the trees created.

OUTPUT:

A generator which creates an exhaustive, duplicate-free listing of the connected free (unlabeled) trees with vertices number of vertices. A tree is a graph with no cycles.

ALGORITHM:

Uses an algorithm that generates each new tree in constant time. See the documentation for, and implementation of, the sage.graphs.trees module, including a citation.

EXAMPLES:

We create an iterator, then loop over its elements.

sage: tree_iterator = graphs.trees(7)
sage: for T in tree_iterator:
...     print T.degree_sequence()
[2, 2, 2, 2, 2, 1, 1]
[3, 2, 2, 2, 1, 1, 1]
[3, 2, 2, 2, 1, 1, 1]
[4, 2, 2, 1, 1, 1, 1]
[3, 3, 2, 1, 1, 1, 1]
[3, 3, 2, 1, 1, 1, 1]
[4, 3, 1, 1, 1, 1, 1]
[3, 2, 2, 2, 1, 1, 1]
[4, 2, 2, 1, 1, 1, 1]
[5, 2, 1, 1, 1, 1, 1]
[6, 1, 1, 1, 1, 1, 1]


The number of trees on the first few vertex counts. This is sequence A000055 in Sloane’s OEIS.

sage: [len(list(graphs.trees(i))) for i in range(0, 15)]
[1, 1, 1, 1, 2, 3, 6, 11, 23, 47, 106, 235, 551, 1301, 3159]

sage.graphs.graph_generators.canaug_traverse_edge(g, aut_gens, property, dig=False, loops=False, implementation='c_graph', sparse=True)

Main function for exhaustive generation. Recursive traversal of a canonically generated tree of isomorph free graphs satisfying a given property.

INPUT:

• g - current position on the tree.
• aut_gens - list of generators of Aut(g), in list notation.
• property - check before traversing below g.

EXAMPLES:

sage: from sage.graphs.graph_generators import canaug_traverse_edge
sage: G = Graph(3)
sage: list(canaug_traverse_edge(G, [], lambda x: True))
[Graph on 3 vertices, ... Graph on 3 vertices]


The best way to access this function is through the graphs() iterator:

Print graphs on 3 or less vertices.

sage: for G in graphs(3):
...    print G
...
Graph on 3 vertices
Graph on 3 vertices
Graph on 3 vertices
Graph on 3 vertices


Print digraphs on 3 or less vertices.

sage: for G in digraphs(3):
...    print G
...
Digraph on 3 vertices
Digraph on 3 vertices
...
Digraph on 3 vertices
Digraph on 3 vertices

sage.graphs.graph_generators.canaug_traverse_vert(g, aut_gens, max_verts, property, dig=False, loops=False, implementation='c_graph', sparse=True)

Main function for exhaustive generation. Recursive traversal of a canonically generated tree of isomorph free (di)graphs satisfying a given property.

INPUT:

• g - current position on the tree.
• aut_gens - list of generators of Aut(g), in list notation.
• max_verts - when to retreat.
• property - check before traversing below g.
• degree_sequence - specify a degree sequence to try to obtain.

EXAMPLES:

sage: from sage.graphs.graph_generators import canaug_traverse_vert
sage: list(canaug_traverse_vert(Graph(), [], 3, lambda x: True))
[Graph on 0 vertices, ... Graph on 3 vertices]


The best way to access this function is through the graphs() iterator:

Print graphs on 3 or less vertices.

sage: for G in graphs(3, augment='vertices'):
...    print G
...
Graph on 0 vertices
Graph on 1 vertex
Graph on 2 vertices
Graph on 3 vertices
Graph on 3 vertices
Graph on 3 vertices
Graph on 2 vertices
Graph on 3 vertices


Print digraphs on 2 or less vertices.

sage: for D in digraphs(2, augment='vertices'):
...    print D
...
Digraph on 0 vertices
Digraph on 1 vertex
Digraph on 2 vertices
Digraph on 2 vertices
Digraph on 2 vertices

sage.graphs.graph_generators.check_aut(aut_gens, cut_vert, n)

Helper function for exhaustive generation.

At the start, check_aut is given a set of generators for the automorphism group, aut_gens. We already know we are looking for an element of the auto- morphism group that sends cut_vert to n, and check_aut generates these for the canaug_traverse function.

EXAMPLE: Note that the last two entries indicate that none of the automorphism group has yet been searched - we are starting at the identity [0, 1, 2, 3] and so far that is all we have seen. We return automorphisms mapping 2 to 3.

sage: from sage.graphs.graph_generators import check_aut
sage: list( check_aut( [ [0, 3, 2, 1], [1, 0, 3, 2], [2, 1, 0, 3] ], 2, 3))
[[1, 0, 3, 2], [1, 2, 3, 0]]

sage.graphs.graph_generators.check_aut_edge(aut_gens, cut_edge, i, j, n, dig=False)

Helper function for exhaustive generation.

At the start, check_aut_edge is given a set of generators for the automorphism group, aut_gens. We already know we are looking for an element of the auto- morphism group that sends cut_edge to {i, j}, and check_aut generates these for the canaug_traverse function.

EXAMPLE: Note that the last two entries indicate that none of the automorphism group has yet been searched - we are starting at the identity [0, 1, 2, 3] and so far that is all we have seen. We return automorphisms mapping 2 to 3.

sage: from sage.graphs.graph_generators import check_aut
sage: list( check_aut( [ [0, 3, 2, 1], [1, 0, 3, 2], [2, 1, 0, 3] ], 2, 3))
[[1, 0, 3, 2], [1, 2, 3, 0]]
`