Community detection in large-scale social networks: state-of-the-art and future directions

Abstract

Community detection is an important research area in social networks analysis where we are concerned with discovering the structure of the social network. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is an NP-hard problem and not yet solved to a satisfactory level. This computational complexity is hampered by two major factors. The first factor is related to the huge size of nowadays social networks like Facebook and Twitter reaching billions of nodes. The second factor is related to the dynamic nature of social networks whose structure evolves over time. For this, community detection in social networks analysis is gaining increasing attention in the scientific community and a lot of research was done in this area. The main goal of this paper is to give a comprehensive survey of community detection algorithms in social graphs. For this, we provide a taxonomy of existing models based on the computational nature (either centralized or distributed) and thus in static and dynamic social networks. In addition, we provide a comprehensive overview of existing applications of community detection in social networks. Finally, we provide further research directions as well as some open challenges.