Social Network Analysis - Community Detection: Investigating social network analysis techniques for detecting communities and analyzing network structures in social media data

Authors

  • Dr. Pierre Bourque Professor of Geomatics Engineering, Laval University, Canada Author

Keywords:

Social Network Analysis

Abstract

Social Network Analysis (SNA) has emerged as a powerful tool for understanding the structure and dynamics of social networks. One key aspect of SNA is community detection, which aims to identify groups of nodes within a network that are more densely connected to each other than to the rest of the network. In recent years, with the proliferation of social media platforms, the analysis of social networks has become increasingly important for understanding online communities and their behaviors. This paper provides a comprehensive review of the techniques and methods used in social network analysis for community detection, with a focus on their application to social media data. We discuss the challenges and opportunities in community detection, the different approaches and algorithms used, and the evaluation metrics employed to assess the quality of detected communities. Additionally, we explore the practical implications of community detection in social media, including its role in marketing, recommendations, and understanding online social behavior. Through this review, we aim to provide insights into the state-of-the-art in community detection in social networks and to highlight areas for future research.

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Published

12-09-2023

How to Cite

[1]
Dr. Pierre Bourque, “Social Network Analysis - Community Detection: Investigating social network analysis techniques for detecting communities and analyzing network structures in social media data”, J. of Artificial Int. Research and App., vol. 3, no. 2, pp. 140–151, Sep. 2023, Accessed: Nov. 23, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/170

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