cdlib.evaluation.omega

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

Index of resemblance for overlapping, complete coverage, network clusterings.

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.omega(louvain_communities,leiden_communities)
:Reference:
  1. Gabriel Murray, Giuseppe Carenini, and Raymond Ng. 2012. Using the omega index for evaluating abstractive algorithms detection. In Proceedings of Workshop on Evaluation Metrics and System Comparison for Automatic Summarization. Association for Computational Linguistics, Stroudsburg, PA, USA, 10-18.