Open AccessCommunity Detection Algorithm in Social Networks through Iterative Analysis based on Degree of Nodes
Author(s) -
Amedapu Srinivas,
R. Leela Velusamy
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1021.109119
Subject(s) - cluster analysis , node (physics) , computer science , set (abstract data type) , degree (music) , community structure , identification (biology) , hierarchical clustering , complex network , hierarchical clustering of networks , centroid , clique percolation method , social network (sociolinguistics) , data mining , social network analysis , artificial intelligence , mathematics , correlation clustering , canopy clustering algorithm , world wide web , statistics , engineering , social media , physics , botany , structural engineering , acoustics , biology , programming language
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.