CGraM: Enhanced Algorithm for Community Detection in Social Networks Kalaichelvi Nallusamy * and K. S. Easwarakumar Department of Computer Science and Engineering, College of Engineering, Guindy, Anna University, Chennai, Tamilnadu, India *Corresponding Author: Kalaichelvi Nallusamy. Email: pnkalai@gmail.com Received: 13 May 2021; Accepted: 17 June 2021 Abstract: Community Detection is used to discover a non-trivial organization of the network and to extract the special relations among the nodes which can help in understanding the structure and the function of the networks. However, commu- nity detection in social networks is a vast and challenging task, in terms of detected communities accuracy and computational overheads. In this paper, we propose a new algorithm Enhanced Algorithm for Community Detection in Social Networks – CGraM, for community detection using the graph measures eccentri- city, harmonic centrality and modularity. First, the centre nodes are identified by using the eccentricity and harmonic centrality, next a preliminary community structure is formed by finding the similar nodes using the jaccard coefficient. Later communities are selected from the preliminary community structure based on the number of inter-community and intra-community edges between them. Then the selected communities are merged till the modularity improves to form the better resultant community structure. This method is tested on the real net- works and the results are evaluated using the evaluation metrics modularity and Normalized Mutual Information (NMI). The results are visualized and also com- pared with the state-of-the-art algorithms that covers louvian, walktrap, infomap, label propagation, fast greedy and eigen vector for more accurate analysis. CGraM achieved the better modularity and improved NMI values comparatively with other algorithms and gives improved results collaboratively when compared to previous methods. Keywords: Social network; community detection; eccentricity; centrality; jaccard co-efficient; modularity 1 Introduction As we are aligning towards online for every day to day activities, the demand on social data spike higher and higher. People interaction, opinion on products and policies are happening over the web in the form of social platforms, thus in fact gets as a source data for everything from an ordinary petty shop merchandiser to a head of state. Social platforms take everything to a global stage, where there are no demographic limitations. Today’ s internet trend has led to a virtualized social world where people form groups, communities to interact and share information and this form of networking is widely called a social network. To name a few This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Intelligent Automation & Soft Computing DOI:10.32604/iasc.2022.020189 Article ech T Press Science