Modularity based Community Detection in Social Networks
Shyam Sundar Meena1, Vrinda Tokekar2
1Shyam Sundar Meena*, Ph.D. Scholar, Institute of Engineering & Technology, Devi Ahilya Vishwavidyalaya, Indore, India.
2Dr.(Mrs.) Vrinda Tokekar, Professor, Department of Information Technology, Institute of Engineering & Technology, Devi Ahilya Vishwavidyalaya, Indore, India.
Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 564-569 | Volume-9 Issue-3, February, 2020. | Retrieval Number: B3382129219/2020©BEIESP | DOI: 10.35940/ijeat.B3382.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The community detection is an interesting and highly focused area in the analysis of complex networks (CNA). It identifies closely connected clusters of nodes. In recent years, several approaches have been proposed for community detection and validation of the result. Community detection approaches that use modularity as a measure have given much weight-age by the research community. Various modularity based community detection approaches are given for different domains. The network in the real-world may be weighted, heterogeneous or dynamic. So, it is inappropriate to apply the same algorithm for all types of networks because it may generate incorrect result. Here, literature in the area of community detection and the result evaluation has been extended with an aim to identify various shortcomings. We think that the contribution of facts given in this paper can be very useful for further research.
Keywords: Community detection, Networks, Modularity, NMI.