Community Detection Methods Based on Exploiting Attributes and Interactions on Social Networks: A Survey and Future Directions

Abstract

In recent years, community detection in social network analysis has been a hot research topic that has attracted much more attention in the scientific community and a large number of articles have already been published in the literature. Community detection problems focus on determining the subgraphs of the network that have dense edges and whose nodes are expected to have similar features. Community detection has significant applications in different domains of social network analysis such as sociology, business, criminal detection and recommendation systems. The computational complexity is hampered by the huge size and the dynamic nature of social networks. This paper introduces a comprehensive survey of community detection methods in social networks based on mining attributes and interactions of users. We divide the community detection approaches into three basic categories, namely (1) node content-based approaches, (2) edge content-based approaches, and (3) node- and edge content-based approaches. We also discuss in detail the main idea of each method that is suitable for social network data. Furthermore, to promote the study of community detection, we introduce the benchmark datasets from available sources. Last, we present further research directions as well as some open challenges for the community detection problem in social networks.