A Fast Algorithm for Enumerating Maximal Cliques in Large Scale Network

Social network analysis is one of the most emerging research topics in the field of computer science. Most of the application areas in social network are computationally hard in nature, as the complexity and size of the network are increasing at an exponential rate. Finding maximal clique in large scale network is one of such NP-Hard problems. Clique is defined as the complete subgraph where every node within the subgraph is connected to every other node. Probably, it is the most densely connected subgraph in the network. It may corresponds to group of people who know each other personally in a social network. Maximal clique is the complete subgraph from where if we include an additional node, the completeness property is violated. Maximal clique with maximum number of node is known as maximum clique. Maximum independent set and minimum vertex covering problem are the two variant of maximum clique problem. Identifying maximal cliques in social network may help in understanding the communities evolve in the network. In this paper, a fast and efficient algorithm has been proposed to uncover the hidden cliques in the network by considering the structural attributes in the network. The proposed algorithm has been extensively tested with four real world network datasets. The performance of the algorithm has been compared with few standard algorithms for maximal clique problem.