8.2 A collection of constructors of common graphs

Module: sage.graphs.graph_generators

A collection of constructors of common graphs.

USE:

To see a list of all graph constructors, type "graphs." and then press the tab key. The documentation for each constructor includes information about each graph, which provides a useful reference.

PLOTTING: All graphs (i.e., networks) have an associated SAGE graphics object, which you can display:

sage: G = graphs.WheelGraph(15)
sage: P = G.plot()
sage: P.show()

If you create a graph in SAGE using the Graph command, then plot that graph, the positioning of nodes is determined using the spring-layout algorithm. For the special graph constructors, which you get using graphs.[tab], the positions are preset. For example, consider the Petersen graph with default node positioning vs. the Petersen graph constructed by this database:

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()
sage: petersen_database = graphs.PetersenGraph()
sage: petersen_database.show()

For all the constructors in this database (except the octahedral, dodecahedral, random and empty graphs), the position dictionary is filled in, instead of using the spring-layout algorithm.

For further visual examples and explanation, see the docstrings below, particularly for CycleGraph, StarGraph, WheelGraph, CompleteGraph and CompleteBipartiteGraph.

ORGANIZATION: The constructors available in this database are organized as follows:

        Basic Structures:
            - BarbellGraph
            - BullGraph
            - CircularLadderGraph
            - ClawGraph
            - CycleGraph
            - DiamondGraph
            - EmptyGraph
            - Grid2dGraph
            - GridGraph
            - HouseGraph
            - HouseXGraph
            - KrackhardtKiteGraph
            - LadderGraph
            - LollipopGraph
            - PathGraph
            - StarGraph
            - WheelGraph
        Platonic Solids:
            - TetrahedralGraph
            - HexahedralGraph
            - OctahedralGraph
            - IcosahedralGraph
            - DodecahedralGraph
        Named Graphs:
            - ChvatalGraph
            - DesarguesGraph
            - FlowerSnark
            - FruchtGraph
            - HeawoodGraph
            - MoebiusKantorGraph
            - Pappus Graph
            - PetersenGraph
            - ThomsenGraph
        Families of Graphs:
            - CirculantGraph
            - CompleteGraph
            - CompleteBipartiteGraph
            - CubeGraph
            - BalancedTree
            - LCFGraph
        Pseudofractal Graphs:
            - DorogovtsevGoltsevMendesGraph
        Random Graphs:
            - RandomGNP
            - RandomBarabasiAlbert
            - RandomGNM
            - RandomNewmanWattsStrogatz
            - RandomHolmeKim
            - RandomLobster
            - RandomTreePowerlaw
            - RandomRegular
            - RandomShell
        Random Directed Graphs:
            - RandomDirectedGN
            - RandomDirectedGNC
            - RandomDirectedGNR
        Graphs with a given degree sequence:
            - DegreeSequence
            - DegreeSequenceConfigurationModel
            - DegreeSequenceTree
            - DegreeSequenceExpected

Author Log:

Module-level Functions

canaug_traverse_edge( g, aut_gens, property)

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.

sage: from sage.graphs.graph_generators import canaug_traverse_edge
sage: G = Graph(); G.add_vertices(range(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

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 bipartite graphs on 7 vertices:

sage: L = list( graphs(7, lambda G: G.is_bipartite()) )
sage: len(L)        #   random, due to NetworkX bug: see https://networkx.lanl.gov/ticket/132
29

canaug_traverse_vert( g, aut_gens, max_verts, property)

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.
max_verts
- when to retreat.
property
- check before traversing below g.

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

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 bipartite graphs on up to 7 vertices:

sage: L = list( graphs(7, lambda G: G.is_bipartite(), augment='vertices') )
sage: len(L)        #   random, due to NetworkX bug: see https://networkx.lanl.gov/ticket/132
133

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.

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]]

check_aut_edge( aut_gens, cut_edge, i, j, n)

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.

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]]

Class: GraphGenerators

class GraphGenerators
A class consisting of constructors for several common graphs.

A list of all graphs and graph structures in this database is available via tab completion. Type "graphs." and then hit tab 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.

The constructors currently in this class include:

        Basic Structures:
            - BarbellGraph
            - BullGraph
            - CircularLadderGraph
            - ClawGraph
            - CycleGraph
            - DiamondGraph
            - EmptyGraph
            - Grid2dGraph
            - GridGraph
            - HouseGraph
            - HouseXGraph
            - KrackhardtKiteGraph
            - LadderGraph
            - LollipopGraph
            - PathGraph
            - StarGraph
            - WheelGraph
        Platonic Solids:
            - TetrahedralGraph
            - HexahedralGraph
            - OctahedralGraph
            - IcosahedralGraph
            - DodecahedralGraph
        Named Graphs:
            - ChvatalGraph
            - DesarguesGraph
            - FlowerSnark
            - FruchtGraph
            - HeawoodGraph
            - MoebiusKantorGraph
            - Pappus Graph
            - PetersenGraph
            - ThomsenGraph
        Families of Graphs:
            - CirculantGraph
            - CompleteGraph
            - CompleteBipartiteGraph
            - CubeGraph
            - BalancedTree
            - LCFGraph
        Pseudofractal Graphs:
            - DorogovtsevGoltsevMendesGraph
        Random Graphs:
            - RandomGNP
            - RandomBarabasiAlbert
            - RandomGNM
            - RandomNewmanWattsStrogatz
            - RandomHolmeKim
            - RandomLobster
            - RandomTreePowerlaw
            - RandomRegular
            - RandomShell
        Random Directed Graphs:
            - RandomDirectedGN
            - RandomDirectedGNC
            - RandomDirectedGNR
        Graphs with a given degree sequence:
            - DegreeSequence
            - DegreeSequenceConfigurationModel
            - DegreeSequenceTree
            - DegreeSequenceExpected

Functions: BalancedTree,$  $ BarbellGraph,$  $ BullGraph,$  $ ButterflyGraph,$  $ ChvatalGraph,$  $ CirculantGraph,$  $ CircularLadderGraph,$  $ ClawGraph,$  $ CompleteBipartiteGraph,$  $ CompleteGraph,$  $ CubeGraph,$  $ CycleGraph,$  $ DegreeSequence,$  $ DegreeSequenceConfigurationModel,$  $ DegreeSequenceExpected,$  $ DegreeSequenceTree,$  $ DesarguesGraph,$  $ DiamondGraph,$  $ DodecahedralGraph,$  $ DorogovtsevGoltsevMendesGraph,$  $ EmptyGraph,$  $ FlowerSnark,$  $ FruchtGraph,$  $ Grid2dGraph,$  $ GridGraph,$  $ HeawoodGraph,$  $ HexahedralGraph,$  $ HoffmanSingletonGraph,$  $ HouseGraph,$  $ HouseXGraph,$  $ IcosahedralGraph,$  $ KrackhardtKiteGraph,$  $ LadderGraph,$  $ LCFGraph,$  $ LollipopGraph,$  $ MoebiusKantorGraph,$  $ OctahedralGraph,$  $ PappusGraph,$  $ PathGraph,$  $ PetersenGraph,$  $ RandomBarabasiAlbert,$  $ RandomDirectedGN,$  $ RandomDirectedGNC,$  $ RandomDirectedGNR,$  $ RandomGNM,$  $ RandomGNP,$  $ RandomHolmeKim,$  $ RandomLobster,$  $ RandomNewmanWattsStrogatz,$  $ RandomRegular,$  $ RandomShell,$  $ RandomTreePowerlaw,$  $ StarGraph,$  $ TetrahedralGraph,$  $ ThomsenGraph,$  $ trees,$  $ WheelGraph

BalancedTree( self, 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.

Plot a balanced tree of height 4 with r = 3

sage: G = graphs.BalancedTree(3, 5)
sage: G.plot().show()   # or G.show()

BarbellGraph( self, n1, n2)

Returns a barbell graph with 2*n1 + n2 nodes. 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.

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.

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

sage: g = graphs.BarbellGraph(9,4)
sage: g.show()

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.plot.GraphicsArray(j)
sage: G.show()

BullGraph( self)

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.

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.

Construct and show a bull graph

sage: g = graphs.BullGraph()
sage: g.show()

ButterflyGraph( self, n, [vertices=strings])

Returns a n-dimensional butterfly graph. The vertices consist of pairs (v,i), where v is an n-dimensional tuple (vector) with binary entries (or a string representation of such) and i is an integer in [0..n]. A directed edge goes from (v,i) to (w,i+1) if v and w are identical except for possibly v[i] != w[i].

A butterfly graph has $ (2^n)(n+1)$ vertices and $ n2^{n+1}$ edges.

Input:

vertices
- 'strings' (default) or 'vectors', specifying whether the vertices are zero-one strings or actually tuples over GF(2).

sage: graphs.ButterflyGraph(2).edges(labels=False)
[(('00', 0), ('00', 1)),
(('00', 0), ('10', 1)),
(('00', 1), ('00', 2)),
(('00', 1), ('01', 2)),
(('01', 0), ('01', 1)),
(('01', 0), ('11', 1)),
(('01', 1), ('00', 2)),
(('01', 1), ('01', 2)),
(('10', 0), ('00', 1)),
(('10', 0), ('10', 1)),
(('10', 1), ('10', 2)),
(('10', 1), ('11', 2)),
(('11', 0), ('01', 1)),
(('11', 0), ('11', 1)),
(('11', 1), ('10', 2)),
(('11', 1), ('11', 2))]
sage: graphs.ButterflyGraph(2,vertices='vectors').edges(labels=False)
[(((0, 0), 0), ((0, 0), 1)),
(((0, 0), 0), ((1, 0), 1)),
(((0, 0), 1), ((0, 0), 2)),
(((0, 0), 1), ((0, 1), 2)),
(((0, 1), 0), ((0, 1), 1)),
(((0, 1), 0), ((1, 1), 1)),
(((0, 1), 1), ((0, 0), 2)),
(((0, 1), 1), ((0, 1), 2)),
(((1, 0), 0), ((0, 0), 1)),
(((1, 0), 0), ((1, 0), 1)),
(((1, 0), 1), ((1, 0), 2)),
(((1, 0), 1), ((1, 1), 2)),
(((1, 1), 0), ((0, 1), 1)),
(((1, 1), 0), ((1, 1), 1)),
(((1, 1), 1), ((1, 0), 2)),
(((1, 1), 1), ((1, 1), 2))]

ChvatalGraph( self)

Returns the Chvatal graph.

The Chvatal graph has 12 vertices. It is a 4-regular, 4-chromatic graph. It is one of the few known graphs to satisfy Grunbaum's conjecture that for every m > 1, n > 2, there is an m-regular, m-chromatic graph of girth at least n.

sage: G = graphs.ChvatalGraph()
sage: G.degree()
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]

CirculantGraph( self, n, adjacency)

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.

Filling the position dictionary in advance adds O(n) to the constructor.

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()
sage: posdict23.show()

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.plot.GraphicsArray(j)
sage: G.show()

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.plot.GraphicsArray(j)
sage: G.show()

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)]

CircularLadderGraph( self, n)

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 parrallel 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.

Construct and show a circular ladder graph with 26 nodes

sage: g = graphs.CircularLadderGraph(13)
sage: g.show()

Create several circular ladder graphs in a SAGE graphics array

sage: g = []
sage: j = []
sage: for i in range(9):
...    k = graphs.CircularLadderGraph(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.plot.GraphicsArray(j)
sage: G.show()

ClawGraph( self)

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.

Show a Claw graph

sage: (graphs.ClawGraph()).show()

Inspect a Claw graph

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

CompleteBipartiteGraph( self, 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.

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()
sage: posdict_med.show()

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.plot.GraphicsArray(j)
sage: G.show()

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.plot.GraphicsArray(j)
sage: G.show()

CompleteGraph( self, 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 dependant 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).

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.plot.GraphicsArray(j)
sage: G.show()

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.plot.GraphicsArray(j)
sage: G.show()

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()
sage: posdict23.show()

CubeGraph( self, n)

Author: Robert Miller

PLOTTING: See commented source code.

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.plot.GraphicsArray(j)
sage: G.show(figsize=[6,4])

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)

CycleGraph( self, 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 dependant 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).

Filling the position dictionary in advance adds O(n) to the constructor.

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()
sage: posdict23.show()

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.plot.GraphicsArray(j)
sage: G.show()

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.plot.GraphicsArray(j)
sage: G.show()

DegreeSequence( self, 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 [1].

Input:

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

sage: G = graphs.DegreeSequence([3,3,3,3])
sage: G.plot().show()  # or G.show()

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.plot().show()  # or G.show()

sage: G = graphs.DegreeSequence([4,4,4,4,4,4,4,4])
sage: G.plot().show()  # or G.show()

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

REFERENCE: [1] Chartrand, G. and Lesniak, L. Graphs and Digraphs. Chapman and Hall/CRC, 1996.

DegreeSequenceConfigurationModel( self, 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.

sage: G = graphs.DegreeSequenceConfigurationModel([1,1])
sage: G.adjacency_matrix()
[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.plot().show()  # or G.show()

REFERENCE: [1] Newman, M.E.J. The Structure and function of complex networks, SIAM Review vol. 45, no. 2 (2003), pp. 167-256.

DegreeSequenceExpected( self, 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.

sage: G = graphs.DegreeSequenceExpected([1,2,3,2,3])
sage: G.plot().show()  # or G.show()

REFERENCE: [1] Chung, Fan and Lu, L. Connected components in random graphs with given expected degree sequences. Ann. Combinatorics (6), 2002 pp. 125-145.

DegreeSequenceTree( self, 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.

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

DesarguesGraph( self)

Returns the Desargues graph.

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

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

REFERENCE: [1] Harary, F. Graph Theory. Reading, MA: Addison-Wesley, 1994.

DiamondGraph( self)

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.

Construct and show a diamond graph

sage: g = graphs.DiamondGraph()
sage: g.show()

DodecahedralGraph( self)

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.

Construct and show a Dodecahdedral graph

sage: g = graphs.DodecahedralGraph()
sage: g.show()

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.plot.GraphicsArray(j)
sage: G.show()

DorogovtsevGoltsevMendesGraph( self, n)

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

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).

EmptyGraph( self)

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.

Add one vertex to an empty graph and then show:

sage: empty1 = graphs.EmptyGraph()
sage: empty1.add_vertex()
sage: empty1.show()

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

sage: empty2 = graphs.EmptyGraph()
sage: for i in range(5):
...    empty2.add_vertex() # add 5 nodes, labeled 0-4
...
sage: for i in range(3):
...    empty2.add_edge(i,i+1) # add edges {[0:1],[1:2],[2:3]}
...
sage: for i in range(4)[1:]:
...    empty2.add_edge(4,i) # add edges {[1:4],[2:4],[3:4]}
...
sage: empty2.show()

FlowerSnark( self)

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 algoirithm. By convention, the nodes are drawn 0-14 on the outer circle, and 15-19 in an inner pentagon.

REFERENCES: [1] Weisstein, E. (1999). "Flower Snark - from Wolfram MathWorld". [Online] Available: http://mathworld.wolfram.com/FlowerSnark.html [2007, February 17]

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()

FruchtGraph( self)

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 dependant 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: [1] Weisstein, E. (1999). "Frucht Graph - from Wolfram MathWorld". [Online] Available: http://mathworld.wolfram.com/FruchtGraph.html [2007, February 17]

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

Grid2dGraph( self, n1, n2)

Returns a 2-dimensional grid graph with n1*n2 nodes (n1 rows and n2 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.

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

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

GridGraph( self, 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.

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

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

HeawoodGraph( self)

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 dependant 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: [1] Weisstein, E. (1999). "Heawood Graph - from Wolfram MathWorld". [Online] Available: http://mathworld.wolfram.com/HeawoodGraph.html [2007, February 17]

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

HexahedralGraph( self)

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.

Construct and show a Hexahedral graph

sage: g = graphs.HexahedralGraph()
sage: g.show()

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.plot.GraphicsArray(j)
sage: G.show()

HouseGraph( self)

Returns a house graph with 5 nodes.

A house graph is named for its shape. It is a triange (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 walll. The fifth node is the top of the roof, connected only to the third and fourth.

Construct and show a house graph

sage: g = graphs.HouseGraph()
sage: g.show()

HouseXGraph( self)

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 walll. The fifth node is the top of the roof, connected only to the third and fourth.

Construct and show a house X graph

sage: g = graphs.HouseXGraph()
sage: g.show()

IcosahedralGraph( self)

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.

Construct and show an Octahedral graph

sage: g = graphs.IcosahedralGraph()
sage: g.show()

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.plot.GraphicsArray(j)
sage: G.show()

KrackhardtKiteGraph( self)

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: [1] Kreps, V. (2002). "Social Network Analysis". [Online] Available: http://www.fsu.edu/ spap/water/network/intro.htm [2007, January 17]

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 eigth (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.

Construct and show a Krackhardt kite graph

sage: g = graphs.KrackhardtKiteGraph()
sage: g.show()

LadderGraph( self, n)

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.

Construct and show a ladder graph with 14 nodes

sage: g = graphs.LadderGraph(7)
sage: g.show()

Create several ladder graphs in a SAGE graphics array

sage: g = []
sage: j = []
sage: for i in range(9):
...    k = graphs.LadderGraph(i+2)
...    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.plot.GraphicsArray(j)
sage: G.show()

LCFGraph( self, 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.

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.plot().show()  # or G.show()

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 Polytopes. 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.

LollipopGraph( self, 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.

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

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

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.plot.GraphicsArray(j)
sage: G.show()

MoebiusKantorGraph( self)

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: Upon construction, the position dictionary is filled to overwrite the spring-layout algorithm. By convention, the first 8 nodes are drawn counter-clockwise in an outer circle, with the remaining eight drawn likewise nested in a smaller circular pattern. The Moebius-Kantor graph is constructed directly below from a dictionary with nodes as keys and entries represented the nodes they are connected to. Please browse this dictionary or display an example to further understand the plotting convention.

REFERENCES: [1] Weisstein, E. (1999). "Moebius-Kantor Graph - from Wolfram MathWorld". [Online] Available: http://mathworld.wolfram.com/Moebius-KantorGraph.html [2007, February 17]

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()

OctahedralGraph( self)

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.

Construct and show an Octahedral graph

sage: g = graphs.OctahedralGraph()
sage: g.show()

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.plot.GraphicsArray(j)
sage: G.show()

PappusGraph( self)

Returns the Pappus graph, a graph on 18 vertices.

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

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

PathGraph( self, 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.

Show default drawing by size: 'line': n < 11

sage: p = graphs.PathGraph(10)
sage: p.show()

'circle': 10 < n < 41

sage: q = graphs.PathGraph(25)
sage: q.show()

'line': n > 40

sage: r = graphs.PathGraph(55)
sage: r.show()

Override the default drawing:

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

PetersenGraph( self)

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: When plotting the Petersen graph with the spring-layout algorithm, we see that this graph is not very symmetric and thus the display may not be very meaningful. Efficiency of construction and plotting is not an issue, as the Petersen graph only has 10 vertices.

Our labeling convention here is to start on the outer pentagon from the top, moving counterclockwise. Then the nodes on the inner star, starting at the top and moving counterclockwise.

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()
sage: petersen_database = graphs.PetersenGraph()
sage: petersen_database.show()

RandomBarabasiAlbert( self, 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 veritces 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

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

sage: ba = graphs.RandomBarabasiAlbert(12,3)
sage: ba.plot().show()  # or ba.show()

We view many random graphs using a graphics array:

sage: g = []
sage: j = []
sage: for i in range(9):
...    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.plot.GraphicsArray(j)
sage: G.show()  # or G.show()

RandomDirectedGN( self, n, [kernel=<function <lambda> at 0x2b99d670f9b0>], [seed=None])

Returns a random GN (growing network) digraph with n vertices.

The digraph is constructed by adding vertices with a link to one previously added vertex. The vertex to link to is chosen with a preferential attachment model, i.e. probability is proportional to degree. The default attachment kernel is a linear function of degree. The digraph is always a tree, so in particular it is a directed acyclic graph.

Input:

n
- number of vertices.
kernel
- the attachment kernel
seed
- for the random number generator

sage: D = graphs.RandomDirectedGN(25)
sage: D.plot().show()  # or D.show()

REFERENCE: [1] Krapivsky, P.L. and Redner, S. Organization of Growing Random Networks, Phys. Rev. E vol. 63 (2001), p. 066123.

RandomDirectedGNC( self, n, [seed=None])

Returns a random GNC (growing network with copying) digraph with n vertices.

The digraph is constructed by adding vertices with a link to one previously added vertex. The vertex to link to is chosen with a preferential attachment model, i.e. probability is proportional to degree. The new vertex is also linked to all of the previously added vertex's successors.

Input:

n
- number of vertices.
seed
- for the random number generator

sage: D = graphs.RandomDirectedGNC(25)
sage: D.plot().show()  # or D.show()

REFERENCE: [1] Krapivsky, P.L. and Redner, S. Network Growth by Copying, Phys. Rev. E vol. 71 (2005), p. 036118.

RandomDirectedGNR( self, n, p, [seed=None])

Returns a random GNR (growing network with redirection) digraph with n vertices and redirection probability p.

The digraph is constructed by adding vertices with a link to one previously added vertex. The vertex to link to is chosen uniformly. With probability p, the arc is instead redirected to the successor vertex. The digraph is always a tree.

Input:

n
- number of vertices.
p
- redirection probability
seed
- for the random number generator.

sage: D = graphs.RandomDirectedGNR(25, .2)
sage: D.plot().show()  # or D.show()

REFERENCE: [1] Krapivsky, P.L. and Redner, S. Organization of Growing Random Networks, Phys. Rev. E vol. 63 (2001), p. 066123.

RandomGNM( self, 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

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

sage: gnm = graphs.RandomGNM(12, 12)
sage: gnm.plot().show()  # or gnm.show()

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.plot.GraphicsArray(j)
sage: G.show()  # or G.show()

RandomGNP( self, n, p, [seed=None], [fast=True])

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

IMPLEMENTATION: This function calls the NetworkX function fast_gnp_random_graph, unless fast==False, then gnp_random_graph.

REFERENCES: [1] P. Erdos and A. Renyi, On Random Graphs, Publ. Math. 6, 290 (1959). [2] E. N. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).

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

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

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

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.plot.GraphicsArray(j)
sage: G.show()

TIMINGS: The following timings compare the speed with fast==False and fast==True for sparse and dense graphs. (It's no different?)

sage: t=cputime(); regular_sparse = graphs.RandomGNP(389,.22)
sage: cputime(t)     # slightly random
0.2240130000000029

sage: t=cputime(); fast_sparse =  graphs.RandomGNP(389,.22,fast=True)
sage: cputime(t)     # slightly random
0.22401400000000038

sage: t=cputime(); regular_dense = graphs.RandomGNP(389,.88)    # long time
sage: cputime(t)     # slightly random
0.87205499999999958

sage: t=cputime(); fast_dense = graphs.RandomGNP(389,.88,fast=True)    # long time
sage: cputime(t)     # slightly random
0.90005700000000033

RandomHolmeKim( self, n, m, p, [seed=None])

Returns a random graph generated by the Holme and Kim algorithm for graphs with powerlaw 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.

sage: G = graphs.RandomHolmeKim(12, 3, .3)
sage: G.plot().show()  # or G.show()

REFERENCE: [1] Holme, P. and Kim, B.J. Growing scale-free networks with tunable clustering, Phys. Rev. E (2002). vol 65, no 2, 026107.

RandomLobster( self, 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

sage: G = graphs.RandomLobster(9, .6, .3)
sage: G.plot().show()  # or G.show()

RandomNewmanWattsStrogatz( self, 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

sage: G = graphs.RandomNewmanWattsStrogatz(12, 2, .3)       
sage: G.plot().show()  # or G.show()

REFERENCE: [1] 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.

RandomRegular( self, 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

sage: G = graphs.RandomRegular(3, 20)  # VERY random output
sage: if G:
...    G.plot().show()  # or G.show() (random output)

REFERENCES: [1] 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 [2] Steger, A. and Wormald, N. Generating random regular graphs quickly. Prob. and Comp. 8 (1999), pp 377-396.

RandomShell( self, 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

sage: G = graphs.RandomShell([(10,20,0.8),(20,40,0.8)])
sage: G.plot().show()  # or G.show()

RandomTreePowerlaw( self, n, [gamma=3], [tries=100], [seed=None])

Returns a tree with a powerlaw degree distribution. Returns False on failure.

From the NetworkX documentation: A trial powerlaw degree sequence is chosen and then elements are swapped with new elements from a powerlaw 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

sage: G = graphs.RandomTreePowerlaw(15, 2)  # VERY random output
sage: if G:
...    G.plot().show()  # or G.show() (random output)

StarGraph( self, 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 dependant 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.

sage: import networkx

Compare the plots:

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

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.plot.GraphicsArray(j)
sage: G.show()

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.plot.GraphicsArray(j)
sage: G.show()

TetrahedralGraph( self)

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.

Construct and show a Tetrahedral graph

sage: g = graphs.TetrahedralGraph()
sage: g.show()

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.plot.GraphicsArray(j)
sage: G.show()

ThomsenGraph( self)

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.

sage: T = graphs.ThomsenGraph()
sage: T
Thomsen graph: Graph on 6 vertices
sage: T.graph6_string()
'EFz_'
sage: (graphs.ThomsenGraph()).show()

trees( self, vertices, [augment=edges])

Accesses the generator of trees (graphs without cycles). Iterates over distinct, exhaustive representatives.

Input:

vertices
- natural number
augment
- choices:
'vertices'
- augments by adding a vertex, and edges incident to that vertex. In this case, all trees on up to n=vertices are generated.
'edges'
- augments a fixed number of vertices by adding one edge In this case, all trees on exactly n=vertices are generated.

Sloane A000055:

sage: for i in range(0, 10):
...    print len(list(graphs.trees(i)))
1
1
1
1
2
3
6
11
23
47

WheelGraph( self, 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).

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.plot.GraphicsArray(j)
sage: G.show()

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.plot.GraphicsArray(j)
sage: G.show()

Compare the plotting:

sage: n = networkx.wheel_graph(23)
sage: spring23 = Graph(n)
sage: posdict23 = graphs.WheelGraph(23)
sage: spring23.show()
sage: posdict23.show()

Special Functions: __call__

__call__( self, vertices, [property=<function <lambda> at 0x2b99d670fd70>], [augment=edges], [size=None])

Accesses the generator of isomorphism class representatives. Iterates over distinct, exhaustive representatives.

Input:

vertices
- natural number
property
- any property to be tested on graphs before generation.
augment
- choices:
'vertices'
- augments by adding a vertex, and edges incident to that vertex. In this case, all graphs on 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.
'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.

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 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 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://www.research.att.com/ njas/sequences/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 8 vertices: (see http://www.research.att.com/ njas/sequences/A033995)

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

Generate graphs on the fly: (see http://www.research.att.com/ njas/sequences/A000088)

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

REFERENCE: Brendan D. McKay, Isomorph-Free Exhaustive generation. Journal of Algorithms Volume 26, Issue 2, February 1998, pages 306-324.

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