# networkx.algorithms.centrality.information_centrality¶

information_centrality(G, weight=None, dtype=<class 'float'>, solver='lu')

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).

Returns

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

Return type

dictionary

Notes

The algorithm is from Brandes 1.