Towards the analysis of errors in centrality measures in perpetuated networks

Abstract

"Centrality measures are essential tools for analyzing the structure and dynamics of graphs, such as knowledge or social networks. They reveal the significance and influence of individual nodes. However, their accuracy can be influenced by data quality, algorithms, and network properties. This study investigates errors in centrality measures within perpetuated networks. It focuses on network resilience and how these re- sults may be used to develop efficient algorithms for centrality measures. It also investigates how perturbation strategies impact network resilience and predict connectivity in the perturbed network. By employing centrality measures (degree, betweenness, closeness, eigenvector), we identify critical nodes that significantly affect network connectivity and information flow. Additionally, statistical tests (Kolmogorov-Smirnov, Crame ́r-von Mises) assess network robustness and pinpoint critical transition points. This study, by outlining methods for error identification, quantifica- tion, and mitigation, offers valuable insights for enhancing net- work resilience across various domains, including infrastructure design and social network analysis." (Authors‘ abstract; BIBB-Doku)