A Parallel and Efficient Algorithm for Detecting Overlapping Communities in Social Networks
Subject Areas : electrical and computer engineeringMostafa Sabzekar 1 , Shima Baradaran Nejad 2 , Mahdi Khazaiepoor 3 , Mehdi Kherad 4
1 - Department of Computer Engineering, Birjand University of Technology
2 - Department of Computer Engineering, Islamic Azad University, Birjand Branch, Birjand, Iran
3 - Department of Computer Engineering, Islamic Azad University, Birjand Branch, Birjand,Iran
4 - 2- Ph.D. Student of Information Technology, Faculty of Engineering, Department of computer Engineering, University of Qom, Iran
Keywords: Social networks, parallelization, community detection, overlapping communities.,
Abstract :
Social networks are not only tools for communication but also represent one of the key potentials in business and commerce. One of the most significant issues in this field is clustering nodes and extracting effective and useful patterns from them, known as community detection. A major challenge in community detection within social networks is the vast number of nodes, which makes any kind of analysis difficult. Another challenge is the overlap of cluster members, referred to as overlapping communities. In such networks, each node may belong to multiple groups. Considering overlaps between communities—especially in large-scale networks—poses significant challenges in accurately detecting and identifying communities. Therefore, many studies tend to overlook this issue. In this paper, an approach is proposed to address these challenges. The most time-consuming step in the proposed algorithm, identifying influential nodes, is performed in parallel. Moreover, overlaps between communities are taken into account and analyzed. The results of evaluating the proposed method, in comparison with other existing methods, indicate its superiority in terms of the uniformity of the detected communities.