Combinatorial Network Resilience in Knowledge-Based Supply Chains: Optimal Recovery Strategies Under Targeted Attacks

Driven by the digital economy, cross-disciplinary knowledge integration is accelerating, fostering innovation and collaboration across supply chains. This transformation has also intensified dependencies on critical knowledge flows, exposing supply chains to heightened risks of disruption and making resilience an increasingly important managerial and policy concern. To address this challenge, this study examines the robustness and recoverability of knowledge-based supply chains by modeling their underlying knowledge structures as combinatorial networks. Using data from China's strategic emerging industries, we apply percolation models to evaluate network resilience by simulating two primary forms of structural disruption: node failure and edge blockage. To capture how different disruptions propagate through the network, five node-targeted and five edge-targeted attack strategies are simulated, and the corresponding recovery pathways are evaluated. Robustness is validated via two complementary indicators, namely, the relative size of the largest connected component and network efficiency, to ensure the consistency of the results. The findings reveal substantial heterogeneity in the criticality of nodes and edges, depending on network configuration, and demonstrate that tailored recovery strategies can markedly reduce resilience loss. By integrating proactive defense with recovery optimization, the proposed framework both contributes to advancing theoretical approaches and offers practical implications for strengthening the resilience of knowledge-based supply chains.