networkx.algorithms.centrality.current_flow_closeness_centrality

networkx.algorithms.centrality.current_flow_closeness_centrality(G, normalized=True)

Compute current-flow closeness centrality for nodes.

A variant of closeness centrality based on effective resistance between nodes in a network. This metric is also known as information centrality.

Parameters :

G : graph

A networkx graph

normalized : bool, optional

If True the values are normalized by 1/(n-1) where n is the number of nodes in G.

Returns :

nodes : dictionary

Dictionary of nodes with current flow closeness centrality as the value.

See also

closeness_centrality

Notes

The algorithm is from Brandes [R56].

If the edges have a ‘weight’ attribute they will be used as weights in this algorithm. Unspecified weights are set to 1.

See also [R57] for the original definition of information centrality.

References

[R56]

(1, 2) Ulrik Brandes and Daniel Fleischer, Centrality Measures Based on Current Flow. Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS ‘05). LNCS 3404, pp. 533-544. Springer-Verlag, 2005. http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf

[R57]

(1, 2) Stephenson, K. and Zelen, M. Rethinking centrality: Methods and examples. Social Networks. Volume 11, Issue 1, March 1989, pp. 1-37 http://dx.doi.org/10.1016/0378-8733(89)90016-6