International Conference on Information and Knowledge Technology

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Distributed Learning Automata-based Algorithm for Finding K-Clique in Complex Social Networks

Authors :

Mohammad Mehdi Daliri Khomami¹ Alireza Rezvanian² Ali Mohammad Saghiri³ Mohammad Reza Meybodi⁴

1- دانشگاه صنعتی امیرکبیر 2- دانشگاه علم و فرهنگ تهران 3- دانشگاه صنعتی امیرکبیر 4- دانشگاه صنعتی امیرکبیر

Keywords :

Complex Social Network, K-Clique, Clustering, Learning Automata

Abstract :

Maximal clique finding is a fundamental problem in graph theory and has been broadly investigated. However, maximal clique finding is time-consuming due to the problem's nature and always returns tremendous cliques with large overlaps nodes. For this reason, we study a relaxed version of the clique called k-clique in which following up the subset of vertices with size k such that each pair of vertices in this subset has an edge. The k-clique problem has many applications in many domains, such as motif detection, community structure search, finding anomaly in large graphs, and community structure search. In this paper, we proposed a learning automaton based algorithm for finding k-clique in complex social networks. In the proposed algorithm, a network of learning automata is mapped to the input networks. By selecting the proper action from a set of possible selectable actions, the reward and penalized policy detect the k-clique. Also, we applied the k-clique in terms of finding communities in complex social networks. To show the algorithm's effectiveness, several experiments have been conducted to evaluate the performance of the algorithm on real graphs and synthetic graphs, and the results demonstrate the high efficiency and effectiveness of the algorithm