brainconn.centrality.edge_betweenness_wei

edge_betweenness_wei(G)

Edge betweenness centrality is the fraction of all shortest paths in the network that contain a given edge. Edges 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:

• EBC (NxN numpy.ndarray) – edge betweenness centrality matrix
• BC (Nx1 numpy.ndarray) – nodal betweenness centrality vector

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.edge_betweenness_wei

Calculate centrality measures