PUBLISHED PAPERS #10.13
| Yeşim Aygül, Melike Karatay, Onur Ugurlu. Detecting the Most Central Nodes for the Vulnerability of Power Networks |
|---|
| Abstract. Understanding the vulnerability of power networks is crucial for ensuring the stability and efficiency of modern infrastructure. Identifying the most central nodes that significantly impact network resilience helps prevent potential failures and disruptions. In this study, we analyze the effectiveness of three traditional centrality metrics—Degree Centrality, Betweenness Centrality, and Closeness Centrality —in maximizing the Rupture Degree, which assesses the robustness and resilience of a network by analyzing the impact of node removals. By iteratively removing the highest-ranked nodes based on these centrality measures across eight real-world power networks, we assess their impact on RD. The results indicate that DC generally achieves higher RD values compared to BC and CC; however, in certain networks, BC proves to be a more effective indicator of node criticality. These findings highlight the importance of selecting appropriate centrality metrics in vulnerability analysis and provide insights for enhancing the resilience of power networks. |
| Keywords: power networks, network vulnerability, rupture degree, central nodes |
 Download PDF |
| DOI: https://doi.org/10.30546/MaCoSEP2025.1089 |