Graph theory and its role in social network analysis
1 Lecturer in Science Department, Government Polytechnic Kaup-574117, Karnataka, India.
2 lecturer in science Government polytechnic, Holenarasipura - 573211, Karnataka, India.
3 Senior scale lecturer in science Government polytechnic, Malavalli-571430, Karnataka, India.
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(02), 2088-2093
Publication history:
Received on 13 February 2024; Revised 20 February 2024; accepted on 25 February 2024
Abstract:
Social Network Analysis (SNA) has emerged as a crucial tool for understanding relationships and interactions in complex systems, ranging from social media platforms to organizational structures. Graph theory provides the mathematical foundation for SNA, enabling the representation of entities as nodes and their interactions as edges within a network. This paper explores the core principles of graph theory and their application in analyzing structural and dynamic properties of social networks. Key graph metrics such as degree centrality, betweenness, closeness, and eigenvector centrality are discussed in the context of identifying influential nodes and community detection. The paper also investigates various graph structures, including directed, undirected, weighted, and dynamic graphs, commonly found in real-world social datasets. Recent advancements, such as graph neural networks (GNNs), temporal graph analysis, and privacy-preserving computation, are reviewed to highlight emerging research trends. The study concludes that graph theory not only enhances our understanding of complex social systems but also provides powerful tools for predicting behavior, optimizing communication, and designing scalable networked applications.
Keywords:
Graph Theory; Social Network Analysis (SNA); Centrality Measures; Graph Neural Networks; Community Detection; Influence Propagation; Network Topology;Temporal Graphs; Privacy-Preserving Analysis; Complex Networks
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0