teneto.communitydetection

Louvain

make_consensus_matrix(com_membership, th=0.5)

Makes the consensus matrix.

From multiple iterations, finds a consensus partition.

.

com_membership : array

Shape should be node, time, iteration.

th : float

threshold to cancel noisey edges

D : array

consensus matrix

make_temporal_consensus(com_membership)

Matches community labels accross time-points.

Jaccard matching is in a greedy fashiong. Matching the largest community at t with the community at t-1.

Parameters:com_membership (array) – Shape should be node, time. Returns:D – temporal consensus matrix using Jaccard distance Return type:array

temporal_louvain(tnet, resolution=1, intersliceweight=1, n_iter=100, negativeedge='ignore', randomseed=None, consensus_threshold=0.5, temporal_consensus=True, njobs=1)

Louvain clustering for a temporal network.

Parameters:

• tnet (array, dict, TemporalNetwork) – Input network
• resolution (int) – resolution of Louvain clustering ($gamma$)
• intersliceweight (int) – interslice weight of multilayer clustering ($omega$). Must be positive.
• n_iter (int) – Number of iterations to run louvain for
• randomseed (int) – Set for reproduceability
• negativeedge (str) – If there are negative edges, what should be done with them. Options: ‘ignore’ (i.e. set to 0). More options to be added.
• consensus (float (0.5 default)) – When creating consensus matrix to average over number of iterations, keep values when the consensus is this amount.

Returns:

communities – node,time array of community assignment

Return type:

array (node,time)

Notes

References