2024, Vol. 5, Issue 1, Part A
Dynamic graph models for evolving social networks
Author(s): Mahaboob Ali
Abstract: Dynamic graph models provide a powerful framework for analyzing evolving social networks where nodes and relationships change over time, reflecting the fluid nature of human interactions and online communication. Unlike static graphs that capture only a fixed snapshot of connectivity, dynamic models incorporate temporal information to reveal patterns of growth, decline, and structural transformation within networks. This perspective is essential for understanding processes such as community formation, information diffusion, and influence dynamics in both offline and online contexts. By integrating methods from graph theory, probability, and machine learning, dynamic graph models enable scalable representation, real-time analysis, and predictive capabilities for large-scale social data. They offer valuable insights for applications ranging from recommender systems and viral marketing to fraud detection and epidemic modeling. This paper examines the foundations, methodologies, challenges, and future directions of dynamic graph modeling, highlighting its significance in advancing network science and computational social research.
DOI: https://doi.org/10.22271/math.2024.v5.i1a.249
Pages: 93-99 | Views: 438 | Downloads: 161
Download Full Article: Click Here
How to cite this article:
Mahaboob Ali. Dynamic graph models for evolving social networks. Journal of Mathematical Problems, Equations and Statistics. 2024; 5(1): 93-99. DOI: 10.22271/math.2024.v5.i1a.249
