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Evaluating a community detection method involvesmeasuring the extent to which the resulted solution, i.e clustering,is similar to an optimal solution, a ground truth. Differentnormalized similarity indices have been proposed in the literatureto quantify the extent to which two clusterings are similar where1 refers to a perfect agreement between them (i.e the two clusterings are identical) and 0 refers to a perfect disagreement. Whileinterpreting the similarity score 1 seems to be intuitive, it does notseem to be so when the similarity score is otherwise suggestinga level of disagreement between the compared clusterings. Thatis because there is no universal definition of dissimilarity whenit comes to comparing two clusterings. In this paper, we addressthis issue by first providing a taxonomy of similarity indicescommonly used for evaluating community detection solutions.We then elaborate on the meaning of clusterings dissimilarityand the types of possible dissimilarities that can exist among twoclusterings in the context of community detection. We perform anextensive evaluation to study the behaviour of different similarityindices as a function of the dissimilarity type with both disjointand non-disjoint clusterings. We finally provide practitioners withsome insights on which similarity indices to use for the task athand and how to interpret their values