Complex Innovation Networks, Patent Citations and Power Laws

Abstract

We study knowledge and innovation flows as characterized by the network of patent citations and investigate its scale free power law properties. We discuss the importance of the application of complex networks to the understanding of the underlying processes of knowledge exchange and technological innovation. We suggest that this area of research while traditionally investigated via econometric modeling and statistical data analysis may be further examined and explained via a complex network analysis approach using the tools and techniques of statistical mechanics and advanced network analysis. We demonstrate that the citation network is a scale free network. In particular, the network node degree probability distribution follows a power law. In other words, the probability that a patent is highly connected to other patents is statistically more likely than would be expected via random connections and associations. Hence, the network's properties are determined by a relatively small number of highly connected nodes or patents referred to as hubs. We also highlight several potential application areas for further investigation via a complex network analysis approach.