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<p>When it comes to the protection of critical infrastructure, node protection is the main strategy to adopt if link-level reinforcement is not feasible. Current methodologies identify vulnerable nodes through exhaustive screening. However, this approach often leads to over-provisioning of network connectivity beyond what is actually needed for operations. To tackle this fundamental limitation, we introduce the connectivity core—a novel subgraph abstraction that is integrated with tree perimeter analysis to accurately isolate vulnerable nodes. By excluding core nodes from protective interventions while still maintaining the target connectivity thresholds, our framework achieves significant reductions in protective investments. Empirical evaluations show that our method can achieve up to a <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(10^6\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mn>10</mn> <mn>6</mn> </msup> </math></EquationSource> </InlineEquation> times acceleration over exact algorithms on small graphs, with a cost premium of less than 2% for perfect protection. It also achieves more than 99.9% connectivity retention in large-scale networks with one million nodes and outperforms heuristic baselines across all tested scenarios.</p>

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Building highly reliable large-scale network through connectivity core-based node protection

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • Gaojie Li,
  • Kang Chen

摘要

When it comes to the protection of critical infrastructure, node protection is the main strategy to adopt if link-level reinforcement is not feasible. Current methodologies identify vulnerable nodes through exhaustive screening. However, this approach often leads to over-provisioning of network connectivity beyond what is actually needed for operations. To tackle this fundamental limitation, we introduce the connectivity core—a novel subgraph abstraction that is integrated with tree perimeter analysis to accurately isolate vulnerable nodes. By excluding core nodes from protective interventions while still maintaining the target connectivity thresholds, our framework achieves significant reductions in protective investments. Empirical evaluations show that our method can achieve up to a \(10^6\) 10 6 times acceleration over exact algorithms on small graphs, with a cost premium of less than 2% for perfect protection. It also achieves more than 99.9% connectivity retention in large-scale networks with one million nodes and outperforms heuristic baselines across all tested scenarios.