Scholarlink

<p>This study introduces “Dangling Centrality,” a novel metric for identifying critical nodes in networks by assessing the impact of their link removal on system dynamics. The proposed metric is validated on real-world datasets, including Amazon product networks, a Protein–Protein Interaction (PPI) network, and a Bitcoin network, offering insights into key products, critical proteins, and influential entities. These nodes, as the main pillars of system propagation, are crucial for maintaining the structural and functional integrity of the network. By removing the links of these nodes, the network’s stability, flow, and communication can be disrupted, highlighting their importance. Additionally, small-scale 5-node and 6-node networks are analyzed to demonstrate the metric’s behavior in simpler contexts. Correlation analyses using Pearson’s, Spearman’s, and Kendall’s coefficients demonstrate alignment with traditional centrality metrics while providing a unique perspective. The findings emphasize the metric’s practical utility in understanding network vulnerabilities, enhancing resilience, and informing system design. Materials and implementations are available at: <a href="https://github.com/Ubaidafatima/Centrality-Measures">https://github.com/Ubaidafatima/Centrality-Measures</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Dangling centrality highlights critical nodes by evaluating network stability through link removal

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月20日

查看全文
  • Ubaida Fatima,
  • Saman Hina,
  • Muhammad Wasif

摘要

This study introduces “Dangling Centrality,” a novel metric for identifying critical nodes in networks by assessing the impact of their link removal on system dynamics. The proposed metric is validated on real-world datasets, including Amazon product networks, a Protein–Protein Interaction (PPI) network, and a Bitcoin network, offering insights into key products, critical proteins, and influential entities. These nodes, as the main pillars of system propagation, are crucial for maintaining the structural and functional integrity of the network. By removing the links of these nodes, the network’s stability, flow, and communication can be disrupted, highlighting their importance. Additionally, small-scale 5-node and 6-node networks are analyzed to demonstrate the metric’s behavior in simpler contexts. Correlation analyses using Pearson’s, Spearman’s, and Kendall’s coefficients demonstrate alignment with traditional centrality metrics while providing a unique perspective. The findings emphasize the metric’s practical utility in understanding network vulnerabilities, enhancing resilience, and informing system design. Materials and implementations are available at: https://github.com/Ubaidafatima/Centrality-Measures.