The purpose of this study is to identify and characterize the structure and dynamics of global R&D collaboration networks in ICT by analyzing crosscountry co-patents, with a special focus on the role of China. We employ a Social Network Analysis (SNA) perspective, using information on more than 77 thousand co-patents from 2001-2015. These co-patents are disaggregated by three time periods and four ICT subsectors. Global measures for the network as a whole, as well as local measures on the positioning of countries in the networks are interpreted. The empirical results are highly interesting. First, international R&D collaboration networks in ICT show a dynamic transformation in becoming larger in magnitude (more countries but also more inter-linkages), less centralized and more densely connected, though with varying degrees across ICT subsectors. Second, the powerful position of the US weakens relatively compared to other, increasingly connected countries, in particular China. While China has already surpassed the US in total patenting in ICT in 2015, China is now also catching up from a network perspective shown by its growing central position over the observed time period.

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Cross‐regional R&D networks are essential for regional innovativeness. Yet, we lack insights into technology field‐specific effects of a region's network connectivity. This study investigates key enabling technologies (KETs) to compare knowledge creation effects of EU funded R&D networks for different technological fields. By applying spatially filtered regression models together with marginal effect interpretations for non‐linear models we quantify and compare network effects across KET fields. Results show that the generally positive network effects differ depending on region‐internal endowments and the nature of the underlying technologies. Policy implications arise for the interrelations between EU research, industrial and regional policy.

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Modelling the complex nature of regional knowledge creation is high on the research agenda. It deals with the identification of drivers for regional knowledge creation of different kinds, among them inter-regional networks and agglomeration factors, as well as their interplay; i.e. in which way they influence regional knowledge creation and accordingly, innovation capabilities—in the short- and long-term. Complementing a long line of tradition—establishing a link between regional knowledge input indicators and knowledge output in a regression framework—we propose an empirically founded agent-based simulation model that intends to approximate the complex nature of the multi-regional knowledge creation process for European regions. Specifically, we account for region-internal characteristics, and a specific embedding in the system of region-internal and region-external R&D collaboration linkages. With first exemplary applications, we demonstrate the potential of the model in terms of its robustness and empirical closeness. The model enables the replication of phenomena and current scientific issues of interest in the field of geography of innovation and hence, shows its potential to advance the scientific debate in this field in the future.