Network Analysis with Python

>>> dg = nx.DiGraph()
>>> dg.add_weighted_edges_from([(1, 4, 0.5), (3, 1, 0.75)])
>>> dg.out_degree(1, weight='weight')
0.5
>>> dg.degree(1, weight='weight')
1.25
>>> dg.successors(1)
[4]
>>> dg.predecessors(1)
[3]
21

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Getting started: graph operators
• subgraph(G, nbunch) - induce subgraph of G on nodes in nbunch
• union(G1, G2) - graph union, G1 and G2 must be disjoint
• 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
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Getting started: graph generators 
# small famous graphs
>>> petersen = nx.petersen_graph()
>>> tutte = nx.tutte_graph()
>>> maze = nx.sedgewick_maze_graph()
>>> tet = nx.tetrahedral_graph()
# classic graphs
>>> 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)
# random graphs
>>> 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)
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Getting started: graph input/output
• General read/write
• Read and write edge lists
• Data formats
• Node pairs with no data: 1 2
• Python dictionaries as data: 1 2 {'weight':7, 'color':'green'}
• Arbitrary data: 1 2 7 green