cdlib.evaluation.nf1

cdlib.evaluation.nf1(first_partition: object, second_partition: object) → MatchingResult

Compute the Normalized F1 score of the optimal algorithms matches among the partitions in input. Works on overlapping/non-overlapping complete/partial coverage partitions.

Parameters:

• first_partition – NodeClustering object

• second_partition – NodeClustering object

Returns:

MatchingResult object

Example:

>>> from cdlib import evaluation, algorithms
>>> import networkx as nx
>>> g = nx.karate_club_graph()
>>> louvain_communities = algorithms.louvain(g)
>>> leiden_communities = algorithms.leiden(g)
>>> evaluation.nf1(louvain_communities,leiden_communities)

Reference:

1. Rossetti, G., Pappalardo, L., & Rinzivillo, S. (2016). A novel approach to evaluate algorithms detection internal on ground truth.

2. Rossetti, G. (2017). : RDyn: graph benchmark handling algorithms dynamics. Journal of Complex Networks. 5(6), 893-912.