Comparative evaluation of community detection algorithms: a topological approach

Abstract

Community detection is one of the most active fields in complex networks analysis, due to its potential value in practical applications. Many works inspired by different paradigms are devoted to the development of algorithmic solutions allowing to reveal the network structure in such cohesive subgroups. Comparative studies reported in the literature usually rely on a performance measure considering the community structure as a partition (Rand Index, Normalized Mutual information, etc.). However, this type of comparison neglects the topological properties of the communities. In this article, we present a comprehensive comparative study of a representative set of community detection methods, in which we adopt both types of evaluation. Community-oriented topological measures are used to qualify the communities and evaluate their deviation from the reference structure. In order to mimic realworld systems, we use artificially generated realistic networks. It turns out there is no equivalence between both approaches: a high performance does not necessarily correspond to correct topological properties, and vice-versa. They can therefore be considered as complementary, and we recommend applying both of them in order to perform a complete and accurate assessment.

Local Heuristic Genetic Algorithm for Discovering Community of Complex Networks

“…In recent years, many algorithms [1][2][3] have been proposedto detect communities. For reviews and comparison, one can refer to literature [4] .…”

Controllability and modularity of complex networks

“…Each vertex corresponds to a character, and a link denotes that two characters co-appear in one scene of the novel. The above real-world networks are always used as a benchmark to evaluate the performance of community detection methods [3,5,19]. …”

Investigating community structure in perspective of ego network

“…Numerous algorithms have been developed to reach this goal. Though many shows good quality communities, most of these algorithms detect inaccurate and biased communities (Almeida, Guedes, Meira, & Zaki, 2011;Ebrahimi & Abadeh, 2012;Moradi, Olovsson, & Tsigas, 2012;Orman, Labatut, & Cherifi, 2012). Communities are defined in multiple ways specific to any particular domain, but there is no generalized definition for communities in the network other than the http://dx.doi.org/10.1016/j.eswa.2015.05.009 0957-4174/Ó 2015 Elsevier Ltd. All rights reserved.…”