brainconn.centrality.betweenness_wei

betweenness_wei(G)[source]

Node betweenness centrality is the fraction of all shortest paths in the network that contain a given node. Nodes with high values of betweenness centrality participate in a large number of shortest paths.

Parameters:L (NxN numpy.ndarray) – directed/undirected weighted connection matrix
Returns:BC – node betweenness centrality vector
Return type:Nx1 numpy.ndarray

Notes

The input matrix must be a connection-length matrix, typically obtained via a mapping from weight to length. For instance, in a weighted correlation network higher correlations are more naturally interpreted as shorter distances and the input matrix should consequently be some inverse of the connectivity matrix.

Betweenness centrality may be normalised to the range [0,1] as BC/[(N-1)(N-2)], where N is the number of nodes in the network.

Examples using brainconn.centrality.betweenness_wei