cdlib.evaluation.f1

f1(first_partition: object, second_partition: object) → cdlib.evaluation.comparison.MatchingResult

Compute the average 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
>>> g = nx.karate_club_graph()
>>> louvain_communities = algorithms.louvain(g)
>>> leiden_communities = algorithms.leiden(g)
>>> evaluation.f1(louvain_communities,leiden_communities)

Reference:

1. Rossetti, G., Pappalardo, L., & Rinzivillo, S. (2016). A novel approach to evaluate algorithms detection internal on ground truth. In Complex Networks VII (pp. 133-144). Springer, Cham.