An anytime-anywhere approach for maximal clique enumeration in social network analysis

Social network analysis (SNA) is a set of broadly used techniques designed for analyzing structural information contained in interactions. However, current SNA tools have a poor ability to handle large and dynamic social networks. One particular problem of interest is that of maximal clique enumeration used for studying modularity/community. Critical challenges for this problem include limited scalability and poor ability for handling dynamism. In this paper, we design and implement an anytime anywhere approach for maximal clique enumeration problem in SNA. Through a set of experiments on random graphs, we validate and demonstrate the effectiveness and efficiency of our approach.