Enhanced Clustering Technique for Efficient Identification of Independent Groups in Social Networks

Authors

  • R. Srinivasan Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur – 797 103, Nagaland, India
  • V. Ramachandran Department of Computer Science and Engineering, DMI College of Engineering, Affiliated by Anna University, Chennai – 600 123, Tamilnadu, India
  • Nagaraju Baydeti Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur – 797 103, Nagaland, India https://orcid.org/0000-0001-8870-7841

DOI:

https://doi.org/10.13052/jwe1540-9589.2171

Keywords:

Equivalence Relation, Equivalence Class, Social Networks, Social Media and Clustering

Abstract

The main aim of this paper is to develop a new approach for identifying independent groups among users communicating in social networks using social media applications at any instant. Grouping of users as independent clusters is of dynamic nature as communication between known and unknown users can happen randomly at any point of time. It is becoming inherent to identify the groups, where the members of the group have strong relationship who communicate frequently and consistently via social media applications. Louvain’s algorithm will identify the clusters in the community detection process but keeps the lightweight nodes in the original groups without making them into one group by considering the dependence relations. The concept of Bernstein conditions is enhanced and applied to identify the dependency among the users of social networks by formulating equivalence relations, which adhere to the properties of Reflexivity, Symmetricity and Transitivity. Then, the equivalence classes are identified which denote the individual groups of clusters where the users of one cluster are loosely coupled with the users of any other cluster but tightly coupled among the users of the same group. The strength of relationship among the users within the same and different clusters is identified with respect to the quantum of messages being propagated among the users using Louvain’s algorithm and the results of equivalence class approach are compared using the same set of communication sequences to show the relation dependency among the members in various clusters.

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Author Biographies

R. Srinivasan, Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur – 797 103, Nagaland, India

R. Srinivasan received his master’s degree in Computer Applications from Alagappa University, Karaikudi and master’s degree in Business Administration from Periyar University, Salem and an Executive Diploma from XLRI, Jamshedpur. He is currently a research scholar in Computer Science and Engineering at National Institute of Technology Nagaland. His area of Research work includes Data Analytics, Machine learning and Artificial Intelligence.

V. Ramachandran, Department of Computer Science and Engineering, DMI College of Engineering, Affiliated by Anna University, Chennai – 600 123, Tamilnadu, India

V. Ramachandran received his master’s degree and philosophy of doctorate degree in Electrical Engineering from College of Engineering Guindy, Anna University, Chennai, India. He has 36 years of teaching experience at various levels in the Department of Information Science and Technology, College of Engineering, Anna University, Chennai. He is currently working as Professor in the Department of Computer Science and Engineering, DMI College of Engineering, Chennai, Tamilnadu, India. His research interest includes Cloud Computing, Web Technologies and Internet of Things.

Nagaraju Baydeti, Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur – 797 103, Nagaland, India

Nagaraju Baydeti received his bachelor’s and master’s degrees in the field of Computer Science and Engineering from College of Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India. He received philosophy of doctorate degree in Computer Science and Engineering from National Institute of Technology Nagaland. He is currently working as an Assistant Professor in the Department of Computer Science and Engineering at National Institute of Technology Nagaland. His research interest includes Next Generation Mobile Networks, Wireless Communication and Networks, and Social Network Analysis.

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Published

2022-11-24

Issue

Section

Articles