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# Centrality

## Degree

`degree_centrality`(G) | Compute the degree centrality for nodes.
`in_degree_centrality`(G) | Compute the in-degree centrality for nodes.
`out_degree_centrality`(G) | 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

`current_flow_closeness_centrality`(G[, ...]) | 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

`current_flow_betweenness_centrality`(G[, ...]) | Compute current-flow betweenness centrality for nodes.
`edge_current_flow_betweenness_centrality`(G) | Compute current-flow betweenness centrality for edges.
`approximate_current_flow_betweenness_centrality`(G) | Compute the approximate current-flow betweenness centrality for nodes.
`current_flow_betweenness_centrality_subset`(G, ...) | Compute current-flow betweenness centrality for subsets of nodes.
`edge_current_flow_betweenness_centrality_subset`(G, ...) | Compute current-flow betweenness centrality for edges using subsets of nodes.

## Communicability Betweenness

`communicability_betweenness_centrality`(G) | 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.
`group_degree_centrality`(G, S) | Compute the group degree centrality for a group of nodes.
`group_in_degree_centrality`(G, S) | Compute the group in-degree centrality for a group of nodes.
`group_out_degree_centrality`(G, S) | Compute the group out-degree centrality for a group of nodes.
`prominent_group`(G, k[, weight, C, ...]) | Find the prominent group of size \(k\) in graph \(G\).

## Load

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

## Subgraph

`subgraph_centrality`(G) | Returns subgraph centrality for each node in G.
`subgraph_centrality_exp`(G) | Returns the subgraph centrality for each node of G.
`estrada_index`(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

`second_order_centrality`(G[, weight]) | 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

## Laplacian

`laplacian_centrality`(G[, normalized, ...]) | Compute the Laplacian centrality for nodes in the graph `G`.