2.9. Graph generators and graph operations¶

In addition to constructing graphs node-by-node or edge-by-edge, they can also be generated by

1. Applying classic graph operations, such as:

   subgraph(G, nbunch)      - induce subgraph of G on nodes in nbunch
   union(G1,G2)             - graph union
   disjoint_union(G1,G2)    - graph union assuming all nodes are different
   cartesian_product(G1,G2) - return Cartesian product graph
   compose(G1,G2)           - combine graphs identifying nodes common to both
   complement(G)            - graph complement
   create_empty_copy(G)     - return an empty copy of the same graph class
   convert_to_undirected(G) - return an undirected representation of G
   convert_to_directed(G)   - return a directed representation of G
   

2. Using a call to one of the classic small graphs, e.g.

>>> petersen=nx.petersen_graph()
>>> tutte=nx.tutte_graph()
>>> maze=nx.sedgewick_maze_graph()
>>> tet=nx.tetrahedral_graph()

3. Using a (constructive) generator for a classic graph, e.g.

>>> K_5=nx.complete_graph(5)
>>> K_3_5=nx.complete_bipartite_graph(3,5)
>>> barbell=nx.barbell_graph(10,10)
>>> lollipop=nx.lollipop_graph(10,20)

4. Using a stochastic graph generator, e.g.

>>> er=nx.erdos_renyi_graph(100,0.15)
>>> ws=nx.watts_strogatz_graph(30,3,0.1)
>>> ba=nx.barabasi_albert_graph(100,5)
>>> red=nx.random_lobster(100,0.9,0.9)

5. Reading a graph stored in a file using common graph formats, such as edge lists, adjacency lists, GML, GraphML, pickle, LEDA and others.

>>> nx.write_gml(red,"path.to.file")
>>> mygraph=nx.read_gml("path.to.file")

Details on graph formats: Reading and writing graphs

Details on graph generator functions: Graph generators