Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

# Centrality¶

## Degree¶

 Compute the degree centrality for nodes. Compute the in-degree centrality for nodes. Compute the out-degree centrality for nodes.

## Eigenvector¶

 eigenvector_centrality(G[, max_iter, tol, …]) Compute the eigenvector centrality for the graph G. eigenvector_centrality_numpy(G[, weight, …]) Compute the eigenvector centrality for the graph G. katz_centrality(G[, alpha, beta, max_iter, …]) Compute the Katz centrality for the nodes of the graph G. katz_centrality_numpy(G[, alpha, beta, …]) Compute the Katz centrality for the graph G.

## Closeness¶

 closeness_centrality(G[, u, distance, …]) Compute closeness centrality for nodes. incremental_closeness_centrality(G, edge[, …]) Incremental closeness centrality for nodes.

## Current Flow Closeness¶

 Compute current-flow closeness centrality for nodes. information_centrality(G[, weight, dtype, …]) Compute current-flow closeness centrality for nodes.

## (Shortest Path) Betweenness¶

 betweenness_centrality(G[, k, normalized, …]) Compute the shortest-path betweenness centrality for nodes. betweenness_centrality_subset(G, sources, …) Compute betweenness centrality for a subset of nodes. edge_betweenness_centrality(G[, k, …]) Compute betweenness centrality for edges. edge_betweenness_centrality_subset(G, …[, …]) Compute betweenness centrality for edges for a subset of nodes.

## Current Flow Betweenness¶

 Compute current-flow betweenness centrality for nodes. Compute current-flow betweenness centrality for edges. Compute the approximate current-flow betweenness centrality for nodes. Compute current-flow betweenness centrality for subsets of nodes. Compute current-flow betweenness centrality for edges using subsets of nodes.

## Communicability Betweenness¶

 Returns subgraph communicability for all pairs of nodes in G.

## Group Centrality¶

 group_betweenness_centrality(G, C[, …]) Compute the group betweenness centrality for a group of nodes. group_closeness_centrality(G, S[, weight]) Compute the group closeness centrality for a group of nodes. Compute the group degree centrality for a group of nodes. Compute the group in-degree centrality for a group of nodes. Compute the group out-degree centrality for a group of nodes.

 load_centrality(G[, v, cutoff, normalized, …]) Compute load centrality for nodes. edge_load_centrality(G[, cutoff]) Compute edge load.

## Subgraph¶

 Returns subgraph centrality for each node in G. Returns the subgraph centrality for each node of G. Returns the Estrada index of a the graph G.

## Harmonic Centrality¶

 harmonic_centrality(G[, nbunch, distance]) Compute harmonic centrality for nodes.

## Dispersion¶

 dispersion(G[, u, v, normalized, alpha, b, c]) Calculate dispersion between u and v in G.

## Reaching¶

 local_reaching_centrality(G, v[, paths, …]) Returns the local reaching centrality of a node in a directed graph. global_reaching_centrality(G[, weight, …]) Returns the global reaching centrality of a directed graph.

## Percolation¶

 percolation_centrality(G[, attribute, …]) Compute the percolation centrality for nodes.

## Second Order Centrality¶

 Compute the second order centrality for nodes of G.

## Trophic¶

 trophic_levels(G[, weight]) Compute the trophic levels of nodes. trophic_differences(G[, weight]) Compute the trophic differences of the edges of a directed graph. trophic_incoherence_parameter(G[, weight, …]) Compute the trophic incoherence parameter of a graph.

## VoteRank¶

 voterank(G[, number_of_nodes]) Select a list of influential nodes in a graph using VoteRank algorithm