Normalized Mutual Information to evaluate overlapping community finding algorithms

  title={Normalized Mutual Information to evaluate overlapping community finding algorithms},
  author={Aaron F. McDaid and Derek Greene and Neil J. Hurley},
Given the increasing popularity of algorithms for overlapping clustering, in particular in social network analysis, quantitative measures are needed to measure the accuracy of a method. Given a set of true clusters, and the set of clusters found by an algorithm, these sets of clusters must be compared to see how similar or different the sets are. A normalized measure is desirable in many contexts, for example assigning a value of 0 where the two sets are totally dissimilar, and 1 where they are… CONTINUE READING
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