Graph Generators

Equibel includes a number of functions that wrap around NetworkX graph generators; these functions invoke certain NetworkX functions, and convert the resulting NetworkX Graph objects to EquibelGraph objects.

Classic Graph Generators

Equibel includes the following generators for classic graph topologies:

Type of Graph	Generator Function
Path Graph	eb.path_graph(n)
Complete Graph	eb.complete_graph(n)
Star Graph	eb.star_graph(n)

eb.path_graph(n) creates an undirected path graph on n nodes, numbered using the integers 0 to n - 1, inclusive. The following invocation creates the path graph 0 <-> 1 <-> 2 <-> 3:

>>> G = eb.path_graph(4)

We can inspect the nodes and edges in this path graph as follows:

>>> G.nodes()
[0, 1, 2, 3]
>>> G.edges()
[(0, 1), (1, 2), (2, 3)]

To create an undirected complete graph on n nodes, use:

>>> G = eb.complete_graph(4)

Again, we inspect the nodes and edges to see what was produced by the generator:

>>> G.nodes()
[0, 1, 2, 3]
>>> G.edges()
[(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]

Following the convention used by NetworkX, the function star_graph(n) creates an undirected star graph on n + 1 nodes, with one central node numbered 0, and n outer nodes numbered by the integers 1 to n, inclusive:

>>> G = eb.star_graph(4)

When inspecting the nodes and edges produced by star_graph(4), note that there are 5 nodes, numbered from 0 to 4, inclusive, and that node 0 is the central node of the star gtaph:

>>> G.nodes()
[0, 1, 2, 3, 4]
>>> G.edges()
[(0, 1), (0, 2), (0, 3), (0, 4)]

In addition to the graph generators provided with Equibel, you can also use any of the graph generators included with NetworkX, which are documented here, via the method described next. The majority of the generator functions in NetworkX have an optional keyword argument called create_using, which enables them to return an object of any type that is a subclass of the NetworkX Graph class. Thus, if you include the argument create_using=EquibelGraph() when calling a NetworkX generator function, the output will be an EquibelGraph object with the desired graph topology.

In fact, the graph generators in Equibel are implemented using this method. For example, the eb.star_graph() function is equivalent to the following NetworkX call:

>>> import networkx as nx
>>> G = nx.star_graph(5, create_using=EquibelGraph())

Alternatively, you can initialize an EquibelGraph object from an existing Graph or EquibelGraph object; this creates a new EquibelGraph by copying the nodes and edges from the existing graph. Thus, you can generate any type of (undirected) NetworkX graph and pass it to the EquibelGraph constructor as follows:

>>> G = EquibelGraph(nx.complete_graph(5))