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# networkx.algorithms.centrality.current_flow_closeness_centrality¶

current_flow_closeness_centrality(G, weight=None, dtype=<class '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. 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). nodes – Dictionary of nodes with current flow closeness centrality as the value. dictionary

Notes

The algorithm is from Brandes [1].