## File: vitality.py

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python-networkx 1.7~rc1-3
 `12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788` ``````""" Vitality measures. """ # Copyright (C) 2010 by # Aric Hagberg # Dan Schult # Pieter Swart # All rights reserved. # BSD license. __author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)', 'Renato Fabbri']) __all__ = ['closeness_vitality'] import networkx as nx def weiner_index(G, weight=None): # compute sum of distances between all node pairs # (with optional weights) weiner=0.0 if weight is None: for n in G: path_length=nx.single_source_shortest_path_length(G,n) weiner+=sum(path_length.values()) else: for n in G: path_length=nx.single_source_dijkstra_path_length(G, n,weight=weight) weiner+=sum(path_length.values()) return weiner def closeness_vitality(G, v=None, weight=None): """Compute closeness vitality for nodes. Closeness vitality at a node is the change in the sum of distances between all node pairs when excluding a that node. Parameters ---------- G : graph A networkx graph v : node, optional Return only the value for node v. weight : None or string, optional If None, edge weights are ignored. Otherwise holds the name of the edge attribute used as weight. Returns ------- nodes : dictionary Dictionary with nodes as keys and closeness vitality as the value. Examples -------- >>> G=nx.cycle_graph(3) >>> nx.closeness_vitality(G) {0: 4.0, 1: 4.0, 2: 4.0} See Also -------- closeness_centrality() Notes ----- """ wig=weiner_index(G,weight) closeness_vitality={} for n in G: # remove edges connected to node n and keep list of edges with data # could remove node n but it doesn't count anyway edges=G.edges(n,data=True) if G.is_directed(): edges+=G.in_edges(n,data=True) G.remove_edges_from(edges) closeness_vitality[n]=wig-weiner_index(G,weight) # add edges and data back to graph G.add_edges_from(edges) if v is not None: return closeness_vitality[v] else: return closeness_vitality ``````