Warning

This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

# Source code for networkx.algorithms.vitality

```
"""
Vitality measures.
"""
# Copyright (C) 2012 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
import networkx as nx
__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
'Renato Fabbri'])
__all__ = ['closeness_vitality']
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
[docs]def closeness_vitality(G, weight=None):
"""Compute closeness vitality for nodes.
Closeness vitality of a node is the change in the sum of distances
between all node pairs when excluding that node.
Parameters
----------
G : graph
weight : None or string (optional)
The name of the edge attribute used as weight. If None the edge
weights are ignored.
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()
References
----------
.. [1] Ulrik Brandes, Sec. 3.6.2 in
Network Analysis: Methodological Foundations, Springer, 2005.
http://books.google.com/books?id=TTNhSm7HYrIC
"""
multigraph = G.is_multigraph()
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
if multigraph:
edges = G.edges(n,data=True,keys=True)
if G.is_directed():
edges += G.in_edges(n,data=True,keys=True)
else:
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)
return closeness_vitality
```