TEMPORAL NETWORK ANALYSIS IN DATA SCIENCE FOR TRACKING THE EVOLUTION AND DIFFUSION OF INFORMATION IN DYNAMIC SYSTEMS
Keywords:
Temporal Network Analysis, Information Diffusion, Dynamic Systems, Time-Varying Graphs, Data Science, Network Evolution, Predictive AnalyticsAbstract
This paper explores the application of temporal network analysis in tracking the evolution and diffusion of information within dynamic systems. By leveraging advanced network models, such as time-varying graphs and dynamic Bayesian networks, this study provides insights into the patterns and mechanisms through which information propagates over time. Data from various domains, including public health and social media, were collected and processed to analyze how interactions within these networks evolve. The findings reveal significant patterns of information flow, including the roles of key nodes and the impact of external stimuli on network behavior. The implications of these dynamics are discussed across different sectors, with a focus on enhancing predictive capabilities and strategic planning. This research highlights the importance of temporal analysis in understanding complex networks and offers a foundation for future studies aiming to improve decision-making processes in real-time scenarios.
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Copyright (c) 2024 Sankar Narayanan (Author)
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