Authors: Vincent Labatut
Addresses: Computer Science Department, Galatasaray University, Çırağan Cad. No. 36, Ortaköy 34349, İstanbul, Turkey
Abstract: Community detection can be considered as a variant of cluster analysis applied to complex networks. For this reason, all existing studies have been using tools derived from this field when evaluating community detection algorithms. However, those are not completely relevant in the context of network analysis, because they ignore an essential part of the available information: the network structure. Therefore, they can lead to incorrect interpretations. In this article, we review these measures, and illustrate this limitation. We propose a modification to solve this problem, and apply it to the three most widespread measures: purity, Rand index and normalised mutual information (NMI). We then perform an experimental evaluation on artificially generated networks with realistic community structure. We assess the relevance of the modified measures by comparison with their traditional counterparts, and also relatively to the topological properties of the community structures. On these data, the modified NMI turns out to provide the most relevant results.
Keywords: complex networks; community detection; evaluation measures; cluster analysis; purity; adjusted Rand index; ARI; normalised mutual information; NMI; network structure; web based communities; online communities; virtual communities.
International Journal of Social Network Mining, 2015 Vol.2 No.1, pp.44 - 63
Received: 22 Mar 2013
Accepted: 12 Apr 2014
Published online: 11 Jun 2015 *