Rajesh Yadav,
- Assistant Professor, Department of Computer Science, SIES College Of Arts, Science & Commerce (Autonomous), Maharashtra, India
Abstract
Centrality, a fundamental concept in social network analysis (SNA), plays a pivotal role in understanding the structural dynamics and information flow within networks. This studyoffers an extensive examination of centrality metrics and their utilization in diverse fields. We begin by defining centrality and exploring its significance in characterizing the importance of nodes within a network. Subsequently, we delve into the literature review by different authors and most employed centrality metrics, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, elucidating their underlying mathematical foundations and practical implications.Furthermore, the studydiscusses the strengths and limitations of each centrality measure, offering insights into their suitability for different network structures and research objectives. Additionally, we examine real-world applications of centrality in fields such as social sciences, epidemiology, organizational behavior, and information retrieval, highlighting the diverse contexts in which centrality analysis proves invaluable.This review serves as a comprehensive resource for researchers, practitioners, and enthusiasts in the field of social network analysis, offering a nuanced understanding of centrality measures and their broad-ranging applications across disciplines. Major focus is to make students in undergraduate degree and post-graduationaware about centrality measures in social network analysis.
Keywords: Degree centrality, betweenness centrality, SNA, centrality metrics
[This article belongs to International Journal of Data Structure Studies (ijdss)]
Rajesh Yadav. Centrality in Social Network Analysis: A Comprehensive Review for Students. International Journal of Data Structure Studies. 2023; 01(02):1-8.
Rajesh Yadav. Centrality in Social Network Analysis: A Comprehensive Review for Students. International Journal of Data Structure Studies. 2023; 01(02):1-8. Available from: https://journals.stmjournals.com/ijdss/article=2023/view=126341
Fetching IP address…
Browse Figures
References
- FreemanLinton C.Centrality in social networks conceptual clarification. Soc Networks.1978; 1(3): 215–239.Available at doi:https://doi.org/10.1016/0378–8733(78)90021–7
- Borgatti Stephen P, Everett Martin G. Models of core/periphery structures. Soc Networks.2000; 21(4): 375–395.Available at doi: https://doi.org/10.1016/S0378–8733(99)00019–2.
- Opsahl Tore, Agneessens Filip, Skvoretz John. Node Centrality in Weighted Networks: Generalizing Degree and Shortest Paths. Soc Networks. 2010; 32(3): 245–251. 10.1016/j.socnet.2010.03.006.
- Brass Daniel J. Being in the Right Place: A Structural Analysis of Individual Influence in an Organization.Adm Sci Q. 1984; 29(4): 518–39. JSTOR, https://doi.org/10.2307/2392937.
- Sabidussi Gert. The centrality index of a graph.Psychometrika.1966; 31(4): 581–603.
- Frost H. (2022). Eigenvector centrality for multilayer networks with dependent node importance.arXiv:2205.01478 [physics.soc-ph.2022 May 3.
- Das Kousik, Sovan Samanta, Madhumangal Pal. Study on centrality measures in social networks: a survey. SocNetw Anal Min.2018; 8(1): 13.
- Landherr Andrea, Bettina Friedl, Julia Heidemann. A critical review of centrality measures in social networks. Wirtschaftsinformatik. (Bus Inf Syst Eng). 2010; 2(6): 371–385.
- Riquelme F, Gonzalez-Cantergiani P, Molinero X, Serna M. Centrality measure in social networks based on linear threshold model. Knowledge-based Systems. Jan 2018; 140: 92–102.
- Valente TW, Coronges K, Lakon C, Costenbader E. How correlated are network centrality measures? Connect (Tor). 2008 Jan 1;28(1):16–26.
Volume | 01 |
Issue | 02 |
Received | September 20, 2023 |
Accepted | October 3, 2023 |
Published | October 25, 2023 |