Community Detection Algorithm in Social Networks through Iterative Analysis based on Degree of Nodes
Amedapu Srinivas1, R. Leela Velusamy2
1Amedapu Srinivas, Full Time Ph.D. Research Scholar, Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, INDIA.
2R. Leela Velusamy, Professor, Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, INDIA.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1892-1898 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1021109119/2019©BEIESP | DOI: 10.35940/ijeat.A1021.109119
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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: Social networking is the grouping of individuals into specific groups, like small rural communities or a neighborhood subdivision. A fundamental problem in the analysis of social networks is the tracking of communities. A community is often defined as a group of network members with stronger ties to members within the group than to members outside the group. The traditional method for identifying communities in networks is hierarchical clustering. Recently, several works have been done in this community identification using different type of clustering algorithm and connectivity-based scoring function. In this paper Random Head Node Technique and Highest Degree Head Node Techniques are proposed to group the nodes into communities. In these techniques best set of centroids are chosen based on the fitness value to cluster the nodes into communities.
Keywords: Network node, clustering, random head node, highest degree head node, social networks, social network community, cluster, community detection.