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This study advances the theoretical understanding of supply chain resilience by introducing a novel analytical framework based on critical point theory of cooperative networks. While traditional resilience metrics often lack precision in identifying network collapse thresholds, our approach employs maximum k-core and maximum eigenvalue as rigorous quantitative indicators to precisely determine critical stability boundaries. Analyzing longitudinal data from the China Stock Market & Accounting Research (CSMAR) database, we document a significant structural transformation in China’s supply chain networks from 2001–2023, evolving from simple linear configurations to complex, interconnected network architectures with enhanced stability properties. This research makes three primary contributions: (1) establishing a mathematically rigorous framework for assessing supply chain network resilience, (2) providing empirical evidence of structural evolution patterns in Chinese industrial networks, and (3) identifying industry-specific resilience factors to inform targeted policy and strategic interventions. These findings bridge theoretical network science with practical supply chain management, offering both analytical tools and strategic insights for enhancing industrial resilience against systemic disruptions.

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Resilience Analysis of Chinese Supply Chain Network Based on Critical Point Theory of Cooperative Network

  • 会议论文

  • 发表日期：

    2026年

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  • Xinyue Sun,
  • Siqing Pang,
  • Yutai Zhang,
  • Liangli Yang,
  • Yihua Zhou,
  • Yixiu Kong

摘要

This study advances the theoretical understanding of supply chain resilience by introducing a novel analytical framework based on critical point theory of cooperative networks. While traditional resilience metrics often lack precision in identifying network collapse thresholds, our approach employs maximum k-core and maximum eigenvalue as rigorous quantitative indicators to precisely determine critical stability boundaries. Analyzing longitudinal data from the China Stock Market & Accounting Research (CSMAR) database, we document a significant structural transformation in China’s supply chain networks from 2001–2023, evolving from simple linear configurations to complex, interconnected network architectures with enhanced stability properties. This research makes three primary contributions: (1) establishing a mathematically rigorous framework for assessing supply chain network resilience, (2) providing empirical evidence of structural evolution patterns in Chinese industrial networks, and (3) identifying industry-specific resilience factors to inform targeted policy and strategic interventions. These findings bridge theoretical network science with practical supply chain management, offering both analytical tools and strategic insights for enhancing industrial resilience against systemic disruptions.