current_flow_closeness_centrality¶
- current_flow_closeness_centrality(G, weight='weight', dtype=<type 'float'>, solver='lu')¶
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
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.
See also
Notes
The algorithm is from Brandes [R182].
See also [R183] for the original definition of information centrality.
References
[R182] (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 [R183] (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