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Gallery of Graph Generators in SAGE 

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= Currently Implemented in Graph Generators = 
= Gallery of Graph Generators in SAGE = 
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Info * The Petersen Graph is a named graph that consists of 10 vertices and 14 edges, usually drawn as a fivepoint 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 springlayout 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 and 14 edges. * 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. Code {{{ pos_dict = {} for i in range(5): x = float(functions.cos(pi/2 + ((2*pi)/5)*i)) y = float(functions.sin(pi/2 + ((2*pi)/5)*i)) pos_dict[i] = [x,y] for i in range(10)[5:]: x = float(0.5*functions.cos(pi/2 + ((2*pi)/5)*i)) y = float(0.5*functions.sin(pi/2 + ((2*pi)/5)*i)) pos_dict[i] = [x,y] P = graph.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]},\ pos=pos_dict, name="Petersen graph") return P }}} ==== Examples ==== Petersen Graph as constructed in this class: {{{ sage: petersen_this = graphs.PetersenGraph() sage: petersen_this.show() }}} attachment:petersen_pos.png Petersen Graph plotted using the spring layout algorithm: {{{ 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() }}} attachment:petersen_spring.png 

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Info * 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 n1 in NetworkX complete_graph() Plotting * Upon construction, the position dictionary is filled to override the springlayout 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 springlayout 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 springlayout works. The edges push outward (everything is connected), causing the graph to appear as a 3dimensional pointy ball. (See examples below). * 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 springlayout 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. Code {{{ import networkx as NX pos_dict = {} for i in range(n): x = float(functions.cos((pi/2) + ((2*pi)/n)*i)) y = float(functions.sin((pi/2) + ((2*pi)/n)*i)) pos_dict[i] = [x,y] G = NX.complete_graph(n) return graph.Graph(G, pos=pos_dict, name="Complete graph on %d vertices"%n) }}} 

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Info * 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 springlayout algorithm. By convention, each barbell graph will be displayed with the two complete graphs in the lowerleft and upperright 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. Code {{{ pos_dict = {} for i in range(n1): x = float(cos((pi/4)  ((2*pi)/n1)*i)  n2/2  1) y = float(sin((pi/4)  ((2*pi)/n1)*i)  n2/2  1) j = n11i pos_dict[j] = [x,y] for i in range(n1+n2)[n1:]: x = float(i  n1  n2/2 + 1) y = float(i  n1  n2/2 + 1) pos_dict[i] = [x,y] for i in range(2*n1+n2)[n1+n2:]: x = float(cos((5*pi/4) + ((2*pi)/n1)*(in1n2)) + n2/2 + 2) y = float(sin((5*pi/4) + ((2*pi)/n1)*(in1n2)) + n2/2 + 2) pos_dict[i] = [x,y] import networkx G = networkx.barbell_graph(n1,n2) return graph.Graph(G, pos=pos_dict, name="Barbell graph") }}} ==== Examples ==== {{{ # Construct and show a barbell graph # Bar = 4, Bells = 9 sage: g = graphs.BarbellGraph(9,4) sage: g.show() }}} attachment here 

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Info * 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 springlayout 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. Code {{{ pos_dict = [[0,0],[1,1],[1,1],[2,2],[2,2]] import networkx G = networkx.bull_graph() return graph.Graph(G, pos=pos_dict, name="Bull Graph") }}} ==== Examples ==== {{{ # Construct and show a bull graph sage: g = graphs.BullGraph() sage: g.show() }}} attachment here 

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Info * 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 springlayout 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 innercircle, moving clockwise after that. The outer circle is drawn with the (n+1)th node at the top, then counterclockwise as well. Code {{{ pos_dict = {} for i in range(n): x = float(cos((pi/2) + ((2*pi)/n)*i)) y = float(sin((pi/2) + ((2*pi)/n)*i)) pos_dict[i] = [x,y] for i in range(2*n)[n:]: x = float(2*(cos((pi/2) + ((2*pi)/n)*(in)))) y = float(2*(sin((pi/2) + ((2*pi)/n)*(in)))) pos_dict[i] = [x,y] import networkx G = networkx.circular_ladder_graph(n) return graph.Graph(G, pos=pos_dict, name="Circular Ladder graph") }}} ==== Examples ==== {{{ # Construct and show a circular ladder graph with 26 nodes sage: g = graphs.CircularLadderGraph(13) sage: g.show() }}} attachment here ADD SAGE LIST OF GRAPHS HERE INSTEAD {{{ # 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(node_size=50, vertex_labels=False)) ... j.append(n) ... sage: G = sage.plot.plot.GraphicsArray(j) sage: G.show() }}} attachment here 

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Info * Returns a cycle graph with n nodes. * A cycle graph is a basic structure which is also typically called an ngon. * This constructor is dependant on vertices numbered 0 through n1 in NetworkX cycle_graph() Plotting * Upon construction, the position dictionary is filled to override the springlayout 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 springlayout 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. Feel free to race the constructors below in the examples section. The much larger difference is the time added by the springlayout 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. Code {{{ import networkx as NX pos_dict = {} for i in range(n): x = float(functions.cos((pi/2) + ((2*pi)/n)*i)) y = float(functions.sin((pi/2) + ((2*pi)/n)*i)) pos_dict[i] = [x,y] G = NX.cycle_graph(n) return graph.Graph(G, pos=pos_dict, name="Cycle graph on %d vertices"%n) }}} ==== Examples ==== The following examples require NetworkX (to use default): {{{ sage: import networkx as NX }}} Compare the constructor speeds. {{{ time n = NX.cycle_graph(3989); spring3989 = Graph(n) }}} CPU time: 0.05 s, Wall time: 0.07 s (Time results will vary.) {{{ time posdict3989 = graphs.CycleGraph(3989) }}} CPU time: 5.18 s, Wall time: 6.17 s (Time results will vary.) Compare the plotting speeds. {{{ sage: n = NX.cycle_graph(23) sage: spring23 = Graph(n) sage: posdict23 = graphs.CycleGraph(23) }}} {{{ time spring23.show() }}} CPU time: 2.04 s, Wall time: 2.72 s (Time results will vary.) attachment:cycle_spr23.png {{{ time posdict23.show() }}} CPU time: 0.57 s, Wall time: 0.71 s[[BR]] (Time results will vary.) attachment:cycl_pd23.png View many cycle graphs as a SAGE Graphics Array. With the position dictionary filled: ADD LIST HERE {{{ sage: g = [] sage: j = [] sage: for i in range(16): ... k = graphs.CycleGraph(i+3) ... g.append(k) ... sage: for i in range(4): ... n = [] ... for m in range(4): ... n.append(g[4*i + m].plot(node_size=50, vertex_labels=False)) ... j.append(n) ... sage: G = sage.plot.plot.GraphicsArray(j) sage: G.show() }}} attachment:cycle_pd_array.png With the springlayout algorithm: ADD LIST HERE {{{ sage: g = [] sage: j = [] sage: for i in range(16): ... spr = NX.cycle_graph(i+3) ... k = Graph(spr) ... g.append(k) ... sage: for i in range(4): ... n = [] ... for m in range(4): ... n.append(g[4*i + m].plot(node_size=50, vertex_labels=False)) ... j.append(n) ... sage: G = sage.plot.plot.GraphicsArray(j) sage: G.show() }}} attachment:cycle_spr_array.png === Diamond Graph === Info * 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 springlayout 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. Code {{{ pos_dict = [[0,1],[1,0],[1,0],[0,1]] import networkx G = networkx.diamond_graph() return graph.Graph(G, pos=pos_dict, name="Diamond Graph") }}} ==== Examples ==== {{{ # Construct and show a diamond graph sage: g = graphs.DiamondGraph() sage: g.show() }}} attachment here === Empty Graphs === Info * 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 springlayout algorithm, unless a position dictionary is specified. Code {{{ return graph.Graph() }}} ==== Examples ==== Add one vertex to an empty graph. {{{ sage: empty1 = graphs.EmptyGraph() sage: empty1.add_vertex() sage: empty1.show() }}} attachment:empty1.png 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 04 ... 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() }}} attachment:empty2.png === Grid2d Graphs === Info * Returns a 2dimensional 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 (noncorner) 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 springlayout 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. Code {{{ pos_dict = {} for i in range(n1): y = i for j in range(n2): x = j pos_dict[i,j] = [x,y] import networkx G = networkx.grid_2d_graph(n1,n2) return graph.Graph(G, pos=pos_dict, name="2D Grid Graph") }}} ==== Examples ==== {{{ # Construct and show a grid 2d graph # Rows = 5, Columns = 7 sage: g = graphs.Grid2dGraph(5,7) sage: g.show() }}} attachment here === House Graph === Info * 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 springlayout algorithm. By convention, the house graph is drawn with the first node in the lowerleft corner of the house, the second in the lowerright corner of the house. The third node is in the upperleft corner connecting the roof to the wall, and the fourth is in the upperright corner connecting the roof to the walll. The fifth node is the top of the roof, connected only to the third and fourth. Code ==== This has been updated! Change! ==== {{{ pos_dict = [[1,0],[1,0],[1,1],[1,1],[0,2]] import networkx G = networkx.house_graph() return graph.Graph(G, pos=pos_dict, name="House Graph") ==== Examples ==== {{{ # Construct and show a house graph sage: g = graphs.HouseGraph() sage: g.show() }}} attachment here === House X Graph === Info * Returns a house X graph with 5 nodes. * A house X graph is a house graph with two additional edges. The upperright corner is connected to the lowerleft. And the upperleft corner is connected to the lowerright. * This constructor depends on NetworkX numeric labeling. Plotting * Upon construction, the position dictionary is filled to override the springlayout algorithm. By convention, the house X graph is drawn with the first node in the lowerleft corner of the house, the second in the lowerright corner of the house. The third node is in the upperleft corner connecting the roof to the wall, and the fourth is in the upperright corner connecting the roof to the walll. The fifth node is the top of the roof, connected only to the third and fourth. ==== Code, has been updated! ==== {{{ pos_dict = [[1,0],[1,0],[1,1],[1,1],[0,2]] import networkx G = networkx.house_x_graph() return graph.Graph(G, pos=pos_dict, name="House Graph") }}} ==== Examples ==== {{{ # Construct and show a house X graph sage: g = graphs.HouseXGraph() sage.: g.show() }}} attachment here === Krackhardt Kite Graph === Info * Returns a Krackhardt kite graph with 10 nodes. * This constructor depends on NetworkX numeric labeling. * 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 * Kreps, V. (2002). "Social Network Analysis". [http://www.fsu.edu/~spap/water/network/intro.htm Link] Plotting * Upon construction, the position dictionary is filled to override the springlayout 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. Code {{{ pos_dict = [[1,4],[1,4],[2,3],[0,3],[2,3],[1,2],[1,2],[0,1],[0,0],[0,1]] import networkx G = networkx.krackhardt_kite_graph() return graph.Graph(G, pos=pos_dict, name="Krackhardt Kite Graph") }}} ==== Examples ==== {{{ # Construct and show a Krackhardt kite graph sage: g = graphs.KrackhardtKiteGraph() sage.: g.show() }}} attachment here === Ladder Graph === === Lollipop Graph === === Path Graph === === Star Graphs === Info * 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 springlayout 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 springlayout algorithm for plotting. As far as display, the springlayout should push all other nodes away from the (0) node, and thus look very similar to this constructor's positioning. * 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 springlayout 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. Code {{{ import networkx as NX pos_dict = {} pos_dict[0] = [0,0] for i in range(n+1)[1:]: x = float(functions.cos((pi/2) + ((2*pi)/n)*(i1))) y = float(functions.sin((pi/2) + ((2*pi)/n)*(i1))) pos_dict[i] = [x,y] G = NX.star_graph(n) return graph.Graph(G, pos=pos_dict, name="Star graph on %d vertices"%(n+1)) }}} ==== Examples ==== The following examples require NetworkX (to use default): {{{ sage: import networkx as NX }}} Compare the constructor speeds. {{{ time n = NX.star_graph(3989); spring3989 = Graph(n) }}} CPU time: 0.08 s, Wall time: 0.10 s (Time Results will vary.) {{{ time posdict3989 = graphs.StarGraph(3989) }}} CPU time: 5.43 s, Wall time: 7.41 s (Time results will vary.) Compare the plotting speeds. {{{ sage: n = NX.star_graph(23) sage: spring23 = Graph(n) sage: posdict23 = graphs.StarGraph(23) }}} {{{ time spring23.show() }}} CPU time: 2.31 s, Wall time: 3.14 s (Time results will vary.) attachment:star_spr23.png {{{ time posdict23.show() }}} CPU time: 0.68 s, Wall time: 0.80 s (Time results will vary.) attachment:star_pd23.png View many star graphs as a SAGE Graphics Array. ===== update to with list ===== With the position dictionary filled: {{{ sage: g = [] sage: j = [] sage: for i in range(16): ... k = graphs.StarGraph(i+3) ... g.append(k) ... sage: for i in range(4): ... n = [] ... for m in range(4): ... n.append(g[4*i + m].plot(node_size=50, vertex_labels=False)) ... j.append(n) ... sage: G = sage.plot.plot.GraphicsArray(j) sage: G.show() }}} attachment:star_array_pd.png With the springlayout algorithm: ===== update with list ===== {{{ sage: g = [] sage: j = [] sage: for i in range(16): ... spr = NX.star_graph(i+3) ... k = Graph(spr) ... g.append(k) ... sage: for i in range(4): ... n = [] ... for m in range(4): ... n.append(g[4*i + m].plot(node_size=50, vertex_labels=False)) ... j.append(n) ... sage: G = sage.plot.plot.GraphicsArray(j) sage: G.show() }}} attachment:star_array_spr.png === Wheel Graphs === Info * 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 springlayout 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 springlayout to give a counterintuitive display. (See Graphics Array examples below). * 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 springlayout 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. Code {{{ import networkx as NX pos_dict = {} pos_dict[0] = [0,0] for i in range(n)[1:]: x = float(functions.cos((pi/2) + ((2*pi)/(n1))*(i1))) y = float(functions.sin((pi/2) + ((2*pi)/(n1))*(i1))) pos_dict[i] = [x,y] G = NX.wheel_graph(n) return graph.Graph(G, pos=pos_dict, name="Wheel graph on %d vertices"%n) }}} ==== Examples ==== The following examples require NetworkX (to use default): {{{ sage: import networkx as NX }}} Compare the constructor speeds. {{{ time n = NX.wheel_graph(3989); spring3989 = Graph(n) }}} CPU time: 0.07 s, Wall time: 0.09 s (Time results will vary.) {{{ time posdict3989 = graphs.WheelGraph(3989) }}} CPU time: 5.99 s, Wall time: 8.74 s (Time results will vary.) Compare the plotting speeds. {{{ sage: n = NX.wheel_graph(23) sage: spring23 = Graph(n) sage: posdict23 = graphs.WheelGraph(23) }}} {{{ time spring23.show() }}} CPU time: 2.24 s, Wall time: 3.00 s (Time results will vary.) attachment:wheel_spr23.png {{{ time posdict23.show() }}} CPU time: 0.68 s, Wall time: 1.14 s (Time results will vary.) attachment:wheel_pd23.png View many wheel graphs as a SAGE Graphics Array. ===== update with list ===== With the position dictionary filled: {{{ sage: g = [] sage: j = [] sage: for i in range(16): ... k = graphs.WheelGraph(i+3) ... g.append(k) ... sage: for i in range(4): ... n = [] ... for m in range(4): ... n.append(g[4*i + m].plot(node_size=50, vertex_labels=False)) ... j.append(n) ... sage: G = sage.plot.plot.GraphicsArray(j) sage: G.show() }}} attachment:wheel_array_pd.png With the springlayout algorithm: ===== update with list ===== {{{ sage: g = [] sage: j = [] sage: for i in range(16): ... spr = NX.wheel_graph(i+3) ... k = Graph(spr) ... g.append(k) ... sage: for i in range(4): ... n = [] ... for m in range(4): ... n.append(g[4*i + m].plot(node_size=50, vertex_labels=False)) ... j.append(n) ... sage: G = sage.plot.plot.GraphicsArray(j) sage: G.show() }}} attachment:wheel_array_spr.png 
== Random Generators == 
Emily Kirkman is working on this project.
The goal of the Graph Generators Class is to implement constructors for many common graphs, as well as thorough docstrings that can be used for reference. The graph generators will grow as the Graph Theory Project does. So please check back for additions and feel free to leave requests in the suggestions section.
We currently have 30 constructors of named graphs and basic structures. Most of these graphs are constructed with a preset dictionary of xy coordinates of each node. This is advantageous for both style and time. (The default graph plotting in SAGE using the springlayout algorithm). SAGE graphs all have an associated graphics object, and examples of plotting options are shown on the graphs below.
As we implement algorithms into the Graph Theory Package, the constructors of known graphs would set their properties upon instantiation as well. For example, if someone created a very large complete bipartite graph and then asked if it is a bipartite graph (not currently implemented), then instead of running through an algorithm to check it, we could return a value set at instantiation. Further, this will improve the reference use of the docstrings as we would list the properties of each named graph.
Due to the volume of graphs now in the generators class, this wiki page is now intended to give status updates and serve as a gallery of graphs currently implemented. To see information on a specific graph, run SAGE or the SAGE [http://sage.math.washington.edu:8100 notebook]. For a list of graph constructos, type "graphs." and hit tab. For docstrings, type the graph name and one question mark (i.e.: "graphs.CubeGraph?") then shift + enter. For source code, do likewise with two question marks.
The SAGE [http://sage.math.washington.edu:9001/graph Graph Theory Project] aims to implement Graph objects and algorithms in ["SAGE"].
Suggestions
 ???
Graphs I Plan to Add
Inherited from NetworkX
 Bipartite Generators
 Balanced tree
 Dorogovstev golstev mendes graph
 Grid (ndim)
 Chvatal
 Desargues
 Pappus
 Sedgewick
 Truncated cube
 Truncated tetrahedron
 Tutte
 Also many more random generators and gens from degree sequence to sort through
Families of Graphs
 Generalized Petersen graphs
 Petersen Graph family
 Trees (Directed – not simple. Maybe Balanced tree constructor and query isTree)
 Cayley (Requires Edge Coloring)
 Paley
Named Graphs
 Brinkman
 Clebsch
 Icosahedron
 Grötzsch graph
 Tutte eightcage
 Szekeres snark
 Thomassen graph
 Johnson (maybe own class)
 Turan
Gallery of Graph Generators in SAGE
Named Graphs
Petersen
Graph Families
Complete Graphs
Basic Structures
Barbell Graph
Bull Graph
Circular Ladder Graph
Cycle Graphs