Title: An improved algorithm for detecting overlapping communities in social network

Authors: Mehjabin Khatoon; W. Aisha Banu

Addresses: Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India ' Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India

Abstract: Social networks contain hidden communities, which used to have some structure and the effort to discover those structures of the communities is a significant step in analysing the large-scale structure of those networks. Till now many algorithms have been developed for the detection of those hidden communities inside the social networks. Community detection algorithms results in either detecting the partitions of the network, i.e., non-overlapping communities or detecting the covers of the node, i.e., overlapping communities. In this paper an algorithm for detecting the overlapping communities has been proposed. The proposed algorithm has been compared with other community detection algorithms based on various functional metrics like modularity, conductance, assortativity and centrality. The proposed algorithm is semi-supervised algorithm and it can be applied to networks of huge number of nodes. The proposed approach can detect the individuals in the social network who sometimes belongs to more than one community.

Keywords: centrality; modularity; overlapping community; social network; community detection; community; paradigm.

DOI: 10.1504/IJAIP.2024.140088

International Journal of Advanced Intelligence Paradigms, 2024 Vol.28 No.3/4, pp.272 - 287

Received: 12 Jun 2018
Accepted: 17 Nov 2018
Published online: 24 Jul 2024 *

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