networkx.algorithms.centrality.current_flow_closeness_centrality¶

current_flow_closeness_centrality
(G, weight=None, dtype=<class 'float'>, solver='lu')[source]¶ Compute currentflow closeness centrality for nodes.
Currentflow 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.
 weight (None or string, optional (default=None)) – If None, all edge weights are considered equal. Otherwise holds the name of the edge attribute used as weight.
 dtype (data type (default=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 of nodes with current flow closeness centrality as the value.
Return type: dictionary
See also
Notes
The algorithm is from Brandes [1].
See also [2] for the original definition of information centrality.
References
[1] Ulrik Brandes and Daniel Fleischer, Centrality Measures Based on Current Flow. Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS ‘05). LNCS 3404, pp. 533544. SpringerVerlag, 2005. http://algo.unikonstanz.de/publications/bfcmbcf05.pdf [2] Karen Stephenson and Marvin Zelen: Rethinking centrality: Methods and examples. Social Networks 11(1):137, 1989. https://doi.org/10.1016/03788733(89)900166