This study utilizes digital transformation and patent data from A-share listed companies on the Shanghai and Shenzhen stock exchanges in China between 2011 and 2021 to examine the influence of digital transformation on the quality of cross-regional collaborative innovation. The findings reveal that the cooperative innovation network exhibits pronounced small-world characteristics. In terms of spatio-temporal evolution, China’s urban collaborative innovation network demonstrates a notable quadrilateral spatial structure and has evolved toward a multicenter pattern. Moreover, the advancement of digital transformation positively contributes to both the quality and quantity of cross-regional cooperative innovation. By enhancing the relational embeddedness among cities, digital transformation facilitates improved outcomes in collaborative innovation. Furthermore, when the volume of digital patent applications surpasses a certain threshold, its positive effect on the quality of cross-regional collaborative innovation accelerates. These results provide empirical evidence from a major emerging economy, offering insights that can inform policies and strategies in other regions undergoing digital transition. The mechanisms identified, such as network structure evolution and relational embeddedness, contribute to a broader understanding of how digital transformation shapes innovation dynamics across geographical boundaries in a globalized knowledge economy.

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.