Border sensitive centrality in global patent citation networks

Abstract

When resources are shared between interacting networks, the importance of each node depends strongly on how collaborative or competitive each sub-network is. In this paper, we develop a new method of measuring centrality in the complex network of patent citations that can take political borders into account, where the national benefit of domestic citations relative to foreign citations can be controlled by a free parameter. We find that while some patent classes are of high importance both in the global and the domestic economy, there often exist patent classes in individual countries that are more central nationally than in global economy. We characterize the most important classes globally and domestically for six different nations, and describe their robustness to various perturbations to the model and to noise.