Title: Exploring community detection algorithms for sustainable social networks

Authors: Tabrej Ahamad Khan; Imran Hussain; Mohd Abdul Ahad; Siddhartha Sankar Biswas

Addresses: Jamia Hamdard, Hamdard Nagar, New Delhi – 110062, India ' Jamia Hamdard, Hamdard Nagar, New Delhi – 110062, India ' Jamia Hamdard, Hamdard Nagar, New Delhi – 110062, India ' Jamia Hamdard, Hamdard Nagar, New Delhi – 110062, India

Abstract: Community detection plays a vital role in understanding the structure and dynamics of social networks. The paper examines the effectiveness of three prominent community detection algorithms - Girvan-Newman, Walktrap, and Fluid Communities - in understanding social network dynamics. Utilising the Nashville Meetup Network dataset, it evaluates the algorithms' performance in identifying cohesive groups within the network. The study analyses results based on modularity, clustering coefficient, and community size distribution. Additionally, it explores different graph layouts to visually represent detected communities. The research aims to provide insights into algorithm strengths and limitations, aiding in selecting suitable community detection methods and graph layouts for similar social network analyses, particularly from online community platforms like Meetup.

Keywords: community detection; social networks; Girvan-Newman algorithm; Walktrap algorithm; Fluid Communities algorithm; network analysis; cohesive groups.

DOI: 10.1504/IJCVR.2026.154155

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.4, pp.385 - 398

Received: 28 Nov 2023
Accepted: 23 Jan 2024
Published online: 15 Jun 2026 *

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