Grouping of Nodes in Social Networks Based on Multiphase Approach

摘要

Background: Recent advances in the field of information and social network has led tothe problem of community detection that has got much attention among the researchers.Objective: This paper focus on community discovery, a fundamental task in network analysis bybalancing both attribute and structural similarity. The attribute similarity is evaluated using the Jaccardcoefficient and Structural similarity is achieved through modularity.Methods: The proposed algorithm is designed for identifying communities in social networks byfusing attribute and structural similarity. The algorithm retains the node which has high influence onthe other nodes within the neighbourhood and subsequently groups the objects based on the similarityof the information among the nodes. The extensive analysis is performed on real world datasetslike Facebook, DBLP, Twitter and Flickr with different sizes that demonstrates the effectiveness andefficiency of the proposed algorithm over the other algorithms.Results: The results depicts that the generated clusters have a good balance between the structuraland attribute with high intracluster similarity and less intracluster similarity. The algorithm helps toachieve faster runtime for moderately-sized datasets and better runtime for large datasets with superiorclustering quality.