A Novel Centrality-Based Attack Simulation: Evaluating Resilience and Vulnerability in China’s Knowledge Networks

Existing research on the vulnerability of knowledge innovation networks (KINs) primarily falls into two categories: random attacks and deliberate attacks. This study introduces a novel centrality-based attack framework for the deliberate attack group and systematically examines the impact of different attack mechanisms (such as loss of connectivity, propagation delays, and structural fragmentation) on KINs. Using SCI/SSCI co-authorship data across 286 Chinese cities, this research evaluated network resilience through six key metrics, average clustering coefficient, average degree, average path length, global efficiency, isolated nodes ratio, and largest connected component ratio, thus revealing three key conclusions: First, network density and regional integration surged after 2015, peaking in localized clustering during 2021–2022; yet, post-2022, structural fragmentation and efficiency declines exposed critical vulnerabilities. Second, network disruption simulations revealed that attacks based on structural fragmentation mechanisms have the most significant impact, demonstrating the need to promote polycentric development to reduce core dependency risks and highlighting the importance of protecting high-centrality nodes such as Beijing, Shanghai, and Shenzhen. Third, the inhibitory effect of economic proximity weakened significantly by 2024, suggesting reduced economic disparity barriers to collaboration. Notably, emerging synergies between geographic and economic proximity enhanced innovation cooperation potential, emphasizing spatial–economic coordination as a crucial factor in development-stage planning. By integrating spatiotemporal dynamics with attack taxonomy, our framework advances network theory and equips policymakers with actionable strategies to optimize knowledge flows and mitigate systemic risks. Bridging structural resilience with innovation-driven development, this study offers new insights into China’s urban knowledge networks.