The meaning of dissimilar: an evaluation of various similarity quantification approaches used to evaluate community detection solutions

Abstract

Evaluating a community detection method involves measuring the extent to which the resulted solution, i.e clustering, is similar to an optimal solution, a ground truth. Different normalized similarity indices have been proposed in the literature to quantify the extent to which two clusterings are similar where 1 refers to a perfect agreement between them (i.e the two clusterings are identical) and 0 refers to a perfect disagreement. While interpreting the similarity score 1 seems to be intuitive, it does not seem to be so when the similarity score is otherwise suggesting a level of disagreement between the compared clusterings. That is because there is no universal definition of dissimilarity when it comes to comparing two clusterings. In this paper, we address this issue by first providing a taxonomy of similarity indices commonly used for evaluating community detection solutions. We then elaborate on the meaning of clusterings dissimilarity and the types of possible dissimilarities that can exist among two clusterings in the context of community detection. We perform an extensive evaluation to study the behaviour of different similarity indices as a function of the dissimilarity type with both disjoint and non-disjoint clusterings. We finally provide practitioners with some insights on which similarity indices to use for the task at hand and how to interpret their values.