Кафедра штучного інтелекту (ШІ)

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Research on centrality measures in scale-free networks
(ХНУРЕ, 2024) Ivashyn, S.
Understanding the behavior of centrality measures in the context of various network models is crucial for grasping the structural properties of complex systems. This study explores the constraints imposed on centrality measures by different network models, revealing the maximum achievable centrality values for individual nodes and for the network as a whole. We adopt a systematic approach, selecting specific network models and centrality metrics, modeling, and assessing centrality through empirical experiments. The study encompasses maximum centrality for nodes, the distribution of node centrality, and network centrality (Freeman's centrality) in various network models and centrality metrics. Additionally, we investigate the possibility of deriving theoretical estimates for these centrality limits. The findings of this study not only contribute to a deeper understanding of network dynamics but also pave the way for further analyses of real networks, allowing for a comparison between model-generated and real networks. This research bridges the gap between theoretical modeling and practical network analysis, offering insights into the fundamental principles governing the structure of complex networks.