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Given the increasing popularity of algorithms for overlapping clustering, inparticular in social network analysis, quantitative measures are needed tomeasure the accuracy of a method. Given a set of true clusters, and the set ofclusters found by an algorithm, these sets of clusters must be compared to seehow similar or different the sets are. A normalized measure is desirable inmany contexts, for example assigning a value of 0 where the two sets aretotally dissimilar, and 1 where they are identical. A measure based onnormalized mutual information, [1], has recently become popular. We demonstrateunintuitive behaviour of this measure, and show how this can be corrected byusing a more conventional normalization. We compare the results to that ofother measures, such as the Omega index [2].

Normalized Mutual Information to evaluate overlapping community finding algorithms