In recent years, a large and growing body of scholarly research in economic geography has focused on analyzing the knowledge flows and innovation linkages at different spatial scales. The recognition that the network of both local and trans-local linkages are important for a regional actor’s access to knowledge and resources has pushed scholars to go beyond the traditional local-global dichotomy and adopt a so-called glocal network view of regional innovation. Along with the emergence of network paradigm, the need to better understand regional innovation patterns is widely recognized. From a theoretical perspective, this study sought to consider the linkage between networks, knowledge flows, and innovation patterns. Taking the Yangtze River Delta urban agglomeration as the object and by using Ucinet and ArcGIS tools, we analyzed the intra- and inter-regional innovation network structure and measured the innovation network status based on a unique co-patent dataset issued by the State Intellectual Property Office of China in 2014. The main findings of this study are as follows. (1) Among the firms within the network, State Grid, Jiangsu Electric Power, Zhejiang Electric Power, Shanghai Electric Power, NARI Group, and Sinopec are found to possess the highest rates of centrality. Among the universities, those actors at the center of the network are Zhejiang University, Southeast University, Shanghai Jiaotong University, Donghua University, and East China University of Science and Technology. Prestigious science and engineering universities, large state-owned enterprises, and sino-foreign joint venture enterprises are clearly the most influential actors within the innovation network of the Yangtze River Delta urban agglomeration. Through their structurally central positions they act as the most important bridging and connecting agents in the process of building innovation networks. (2) Recognition of the importance of external sources of knowledge creates an incentive for cities in the Yangtze River Delta urban agglomeration to maintain tight external links. Inter-city level is the main geographical scale of innovation linkages in the Yangtze River Delta urban agglomeration, while geographical proximity is no longer crucial for the formation of collaboration innovation network. (3) The types of network existing within and across regions and the knowledge flow through these networks will impact on regional innovation patterns. From the perspective of intra- and inter-regional innovation networks, the Yangtze River Delta urban agglomeration presents four different types of regional innovation patterns. The successful innovative cities, such as Shanghai, Nanjing, and Hangzhou, are characterized by a dense local network and involved in external links. Weaker innovation lagging regions are likely to possess poor network connections, be it of a local or trans-local nature. Our empirical work suggests that a key driver of regional innovation consists of the capability of actors in a region to access and subsequently utilize both local and trans-local beneficial knowledge. The findings of this study may provide a reference for the optimization and upgrading of intra- and inter-regional innovation networks.