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<p>Cascading failures pose a significant threat to the stability and functionality of complex systems, making their mitigation a crucial area of research. While existing strategies aim to enhance network robustness, identifying an optimal set of critical nodes that mediates the cascade for protection remains a challenging task. Here, we present a robust and pragmatic framework that effectively mitigates the cascading failures by strategically identifying and securing critical nodes within the network. Our approach leverages a graph coloring technique to identify the critical nodes using the local network topology, and results in a minimal set of critical nodes to be protected yet maximally effective in mitigating the cascade thereby retaining a large fraction of the network intact. Our method outperforms existing mitigation strategies across diverse network configurations and failure scenarios. An extensive empirical validation using real-world networks highlights the practical utility of our framework, offering a promising tool for enhancing network robustness in complex systems.</p>

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Graph coloring framework to mitigate cascading failure in complex networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月17日

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  • Karan Singh,
  • V. K. Chandrasekar,
  • Wei Zou,
  • Jürgen Kurths,
  • D. V. Senthilkumar

摘要

Cascading failures pose a significant threat to the stability and functionality of complex systems, making their mitigation a crucial area of research. While existing strategies aim to enhance network robustness, identifying an optimal set of critical nodes that mediates the cascade for protection remains a challenging task. Here, we present a robust and pragmatic framework that effectively mitigates the cascading failures by strategically identifying and securing critical nodes within the network. Our approach leverages a graph coloring technique to identify the critical nodes using the local network topology, and results in a minimal set of critical nodes to be protected yet maximally effective in mitigating the cascade thereby retaining a large fraction of the network intact. Our method outperforms existing mitigation strategies across diverse network configurations and failure scenarios. An extensive empirical validation using real-world networks highlights the practical utility of our framework, offering a promising tool for enhancing network robustness in complex systems.