Scholarlink

<p>Enhancing the resilience of knowledge networks in urban agglomerations is critical for establishing secure, efficient, and adaptive regional innovation systems. This study develops an integrated analytical framework that combines static and dynamic resilience to capture both structural characteristics and recovery processes. It constructs the knowledge network based on co-authored publications indexed in the Web of Science (WOS), covering 41 cities in the Yangtze River Delta from 2011 to 2020. It applies social network analysis, GIS-based spatial analysis, and disruption—recovery simulation to evaluate both static and dynamic dimensions of knowledge network resilience. A negative binomial regression model is further employed to investigate the driving mechanisms behind resilience evolution. The findings are as follows: (1) At the level of static resilience, the overall resilience of the knowledge network in the Yangtze River Delta urban agglomeration has steadily improved. This is reflected in enhanced structural coordination, improved efficiency of information transmission, reduced network disassortativity, and more pronounced clustering characteristics. (2) Regarding dynamic resilience, the network demonstrates greater buffering and recovery capabilities under both random and targeted disturbances. This indicates a shift from structural robustness toward evolutionary resilience in the regional knowledge system. (3) Regression results indicate that the homophily effect, the Matthew effect, and multidimensional proximity jointly drive the evolution of knowledge network resilience. Industrial and human capital similarities significantly strengthen intercity knowledge collaboration, while economic similarity shows no stable effect. A favorable educational environment promotes cooperation, whereas excessive external openness tends to weaken network resilience. Moreover, multidimensional proximity exhibits heterogeneous nonlinear effects, suggesting that resilience emerges from differentiated balances between geographical, cognitive, social, and cultural proximity rather than uniform closeness. This study deepens theoretical understanding of innovation system resilience and provides empirical evidence for building shock-resistant and adaptive urban knowledge networks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The driving mechanisms of knowledge network resilience evolution: evidence from the Yangtze River Delta urban agglomeration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月29日

查看全文
  • Yushan Wang

摘要

Enhancing the resilience of knowledge networks in urban agglomerations is critical for establishing secure, efficient, and adaptive regional innovation systems. This study develops an integrated analytical framework that combines static and dynamic resilience to capture both structural characteristics and recovery processes. It constructs the knowledge network based on co-authored publications indexed in the Web of Science (WOS), covering 41 cities in the Yangtze River Delta from 2011 to 2020. It applies social network analysis, GIS-based spatial analysis, and disruption—recovery simulation to evaluate both static and dynamic dimensions of knowledge network resilience. A negative binomial regression model is further employed to investigate the driving mechanisms behind resilience evolution. The findings are as follows: (1) At the level of static resilience, the overall resilience of the knowledge network in the Yangtze River Delta urban agglomeration has steadily improved. This is reflected in enhanced structural coordination, improved efficiency of information transmission, reduced network disassortativity, and more pronounced clustering characteristics. (2) Regarding dynamic resilience, the network demonstrates greater buffering and recovery capabilities under both random and targeted disturbances. This indicates a shift from structural robustness toward evolutionary resilience in the regional knowledge system. (3) Regression results indicate that the homophily effect, the Matthew effect, and multidimensional proximity jointly drive the evolution of knowledge network resilience. Industrial and human capital similarities significantly strengthen intercity knowledge collaboration, while economic similarity shows no stable effect. A favorable educational environment promotes cooperation, whereas excessive external openness tends to weaken network resilience. Moreover, multidimensional proximity exhibits heterogeneous nonlinear effects, suggesting that resilience emerges from differentiated balances between geographical, cognitive, social, and cultural proximity rather than uniform closeness. This study deepens theoretical understanding of innovation system resilience and provides empirical evidence for building shock-resistant and adaptive urban knowledge networks.