Community Discovery in Social Networks: Applications, Methods and Emerging Trends

Analysis of interaction networks in the social context can result in the discovery of important patterns and potentially shed light on important properties governing the growth of such networks.

Abstract

Data sets originating from many different real world domains can be represented in the form of interaction networks in a very natural, concise and meaningful fashion. This is particularly true in the social context, especially given recent advances in Internet technologies and Web 2.0 applications leading to a diverse range of evolving social networks. Analysis of such networks can result in the discovery of important patterns and potentially shed light on important properties governing the growth of such networks. It has been shown that most of these networks exhibit strong modular nature or community structure. An important research agenda thus is to identify communities of interest and study their behavior over time. Given the importance of this problem there has been significant activity within this field particularly over the last few years. In this article we survey the landscape and attempt to characterize the principle methods for community discovery (and related variants) and identify current and emerging trends as well as crosscutting research issues within this dynamic field.