networkx.current_flow_closeness_centrality

networkx.current_flow_closeness_centrality(G, weight='weight', dtype=<type 'float'>, solver='lu')[source]

Compute current-flow closeness centrality for nodes.

Current-flow closeness centrality is 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

dtype: data type (float)

Default data type for internal matrices. Set to np.float32 for lower memory consumption.

solver: string (default=’lu’)

Type of linear solver to use for computing the flow matrix. Options are “full” (uses most memory), “lu” (recommended), and “cg” (uses least memory).

Returns:

nodes : dictionary

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

Notes

The algorithm is from Brandes [R877].

See also [R878] for the original definition of information centrality.

References

[R877](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
[R878](1, 2) Karen Stephenson and Marvin Zelen: Rethinking centrality: Methods and examples. Social Networks 11(1):1-37, 1989. http://dx.doi.org/10.1016/0378-8733(89)90016-6