A Unified Framework for Community Structure Analysis in Dynamic Social Networks

Abstract

One of the major tasks related to structural social network analysis is the detection and analysis of community structures, which is highly challenging owing to consideration of various constraints while defining a community. For example, community structures to be detected may be disjointed, overlapping, or hierarchical, whereas on the other hand, community detection methods may vary depending on whether links are weighted or unweighted, directed or undirected, and whether the network is static or dynamic. Although a number of community detection methods exist in literature, most of them address only a particular aspect, and generally, community structures and their evolution analysis are studied separately. However, analyzing community structures and their evolution under a unified framework could be more useful, where community structures provide evidence of community evolution. Moreover, not many researchers have dealt with the issue of the utilization of detected communities and they have simply proposed methods to detect communities without emphasizing their utilities. This chapter presents a unified social network analysis framework that is mainly aimed at addressing the problem of community analysis, including overlapping community detection, community evolution tracking, and hierarchical community structure identification in a unified manner. At the end of this chapter, we present some important application areas in which the knowledge of community structures facilitates the generation of important analytical results and processes to help solve other social network analysis-related problems. The application areas mainly include dealing with spammers, in which we present the importance of various community-based features in learning predictive models for spammer detection in online social networks. In addition, we address the issue of detecting deceptions in online social networks, which mainly includes dealing with cloning attacks, and the importance of community detection for facilitating the process of viral marketing in online social networks.