brainconn.modularity.modularity_finetune_und

modularity_finetune_und(W, ci=None, gamma=1, seed=None)

The optimal community structure is a subdivision of the network into nonoverlapping groups of nodes in a way that maximizes the number of within-group edges, and minimizes the number of between-group edges. The modularity is a statistic that quantifies the degree to which the network may be subdivided into such clearly delineated groups.

This algorithm is inspired by the Kernighan-Lin fine-tuning algorithm and is designed to refine a previously detected community structure.

Parameters:

• W (NxN numpy.ndarray) – undirected weighted/binary connection matrix
• ci (Nx1 numpy.ndarray | None) – initial community affiliation vector
• gamma (float) – resolution parameter. default value=1. Values 0 <= gamma < 1 detect larger modules while gamma > 1 detects smaller modules.
• seed (int | None) – random seed. default value=None. if None, seeds from /dev/urandom.

Returns:

• ci (Nx1 numpy.ndarray) – refined community affiliation vector
• Q (float) – optimized modularity metric

Notes

Ci and Q may vary from run to run, due to heuristics in the algorithm. Consequently, it may be worth to compare multiple runs.