Overlapping Community Detection based on Facets of Social Network: An Empirical Analysis

Abstract

Detection of overlapping communities is a challenging problem that has drawn a lot of research interest. This is motivated by the fact that in real-world networks, individuals frequently join multiple groups subsequently, resulting in overlapping communities. In this paper, we presented a comprehensive analysis of numerous state-of-the-art overlapping community detection algorithms in order to understand the relative efficiency of the corresponding algorithms in handling specific issues. We consider issues like the facets of the social networks that are used for overlapping community detection, time complexity, accuracy, and quality. However, the accuracy and quality metrics are not sufficient to evaluate the comparative performance of community detection algorithms because these measures use an indirect approach for comparing the algorithms. Therefore, we have additionally used a direct evaluation metric namely, topological variance for performance analysis of the community detection algorithms. Experiments are conducted on several widely used real world networks. This study allows us to identify the algorithms that work well in different scenarios. As a result, we arrive at findings that direct our algorithm selection procedure in accordance with predetermined goals.