brainconn.modularity.link_communities

link_communities(W, type_clustering='single')

The optimal community structure is a subdivision of the network into nonoverlapping groups of nodes which maximizes the number of within-group edges and minimizes the number of between-group edges.

This algorithm uncovers overlapping community structure via hierarchical clustering of network links. This algorithm is generalized for weighted/directed/fully-connected networks

Parameters:

• W (NxN np.array) – directed weighted/binary adjacency matrix
• type_clustering (str) – type of hierarchical clustering. ‘single’ for single-linkage, ‘complete’ for complete-linkage. Default value=’single’

Returns:

M – nodal community affiliation matrix.

Return type:

CxN numpy.ndarray