A review of clique-based overlapping community detection algorithms - Gupta, Sumit Kumar; Singh, Dhirendra Pratap; Choudhary, Jaytrilok (Knowledge and information systems, 2022)
Detection of communities is one of the prominent characteristics of vast and complex networks like social networks, collaborative networks, and web graphs. In the modern era, new users get added to these complex networks, which results in an expansion of application-generated networks. Extracting relevant information from these large networks has become one of the most prominent research areas. Community detection tries to reduce the application-generated graph into smaller communities in which nodes within the community are similar. Most of the recent proposals are focused on detecting overlapping communities in the network with higher accuracy. An integral issue in graph theory is the enumeration of cliques in a larger graph. As clique is a group of completely connected nodes which shows the explicit communities means these nodes share the same types of information. Clique-based community detection algorithm utilizing the clique property of the graph also identifies the implicit communities, which is not directly shown in the graph. Many overlapping community detection algorithms are proposed by researchers that rely on cliques. The goal of this paper is to offer a comparative analysis of clique-based community detection algorithms. This paper provides a pervasive survey on research works identifying the cliques in a network for detecting overlapping communities. We bring together most of the state-of-the-art clique-based community detection algorithms into a single article with their accessible benchmark data sets. It presents a detailed description of methods based on K-cliques, maximal cliques, and triad percolation methods and addresses these approaches’ challenges. Finally, the comparative analysis of overlapping community detection methodologies is also reported.