Title: A systematic review and bibliometric analysis of community detection methodologies in dynamic networks

Authors: Namika Makhija; Shashank Mouli Satapathy; Ashish Kumar Dwivedi

Addresses: School of Information Systems and Management Heinz College, Carnegie Mellon University, Pittsburgh – 15213, USA ' Department of Computational Intelligence, School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu – 632014, India ' Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering, Visakhapatnam, India

Abstract: With the growing use of the internet, the number of relationships between individuals has increased in large numbers. This has necessarily paved the way for community detection, which is one of the primary methods to analyse social networks. There have been several methodologies proposed for community detection in the past, which are systematically contrasted against each other in the following sections. The aim of this research article is to analyse the various proposed methods for community detection on six primary basis: most commonly used algorithms, the research interest of the topic, country-based interest, author co-citation network, other investigated domains, and the relationship between citations and year of publications and algorithms. A set of research questions is formulated that addresses the objective of conducting this literature survey. A search strategy is employed to cater to the required set of articles.

Keywords: cocitation networks; community detection; community structure; social networks.

DOI: 10.1504/IJBIS.2020.10023780

International Journal of Business Information Systems, 2021 Vol.38 No.1, pp.34 - 61

Received: 08 Feb 2019
Accepted: 20 Apr 2019

Published online: 30 Oct 2021 *

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