ACSIMCD: A 2-phase framework for detecting meaningful communities in dynamic social networks

Abstract

Detecting and analyzing community structure is a challenging topic in dynamic social network analysis. Although the number of methods in this area is on the rise, there are only a few algorithms that can discover meaningful communities based on different aspects of social networks. Indeed, social networks contain various information sources that can be used to analyze them. The most important part of this information is related to users’ topics of interest (content information) and users’ interactions (structure information). One promising solution to discover meaningful communities is to combine these two concepts. Based on this, we introduce ACSIMCD, a 2-phase framework for discovering and updating community structure without recomputing them from scratch at each snapshot. This article mainly includes two parts. In the first part, a static community detection algorithm which is called Content and Structure Information based Method for Community Detection (CSIMCD for short) is proposed to discover the initial community structure. The CSIMCD uses a hybrid approach founded on statistical and semantic measures to extract the users’ topics of interest. Accordingly, the original network is divided into several clusters (topical clusters) so that each one represents a distinct topic, then by performing a link analysis on each topical cluster, the communities are detected. In the second part, we propose ACSIMCD (Adaptive CSIMCD), an adaptive method for detecting and updating community structure in dynamic social networks. More precisely, the ACSIMCD explores the topics of interest of each changed node to identify the topical cluster it belongs to. After that, we update the community structure in this topical cluster, and we keep others as they are. We compare the ACSIMCD model with algorithms from different approaches including content-based methods on real-world networks. The experimental results showed that ACSIMCD produces a community structure of high quality from the perspective of links and interests compared with the classical methods, and that it is able to process network changes effectively in a reasonable time scale.