Multilayer patent citation networks: A comprehensive analytical framework for studying explicit technological relationships


The use of patent citation networks as research tools is becoming increasingly commonplace in the field of innovation studies. However, these networks rarely consider the contexts in which these citations are generated and are generally restricted to a single jurisdiction. Here, we propose and explore the use of a multilayer network framework that can naturally incorporate citation metadata and stretch across jurisdictions, allowing for a complete view of the global technological landscape that is accessible through patent data. Taking a conservative approach that links citation network layers through triadic patent families, we first observe that these layers contain complementary, rather than redundant, information about technological relationships. To probe the nature of this complementarity, we extract network communities from both the multilayer network and analogous single-layer networks, then directly compare their technological composition with established technological similarity networks. We find that while technologies are more splintered across communities in the multilayer case, the extracted communities match much more closely the established networks. We conclude that by capturing citation context, a multilayer representation of patent citation networks is, conceptually and empirically, better able to capture the significant nuance that exists in real technological relationships when compared to traditional, single-layer approaches. We suggest future avenues of research that take advantage of novel computational tools designed for use with multilayer networks.

Technological Forecasting and Social Change
Martina Contisciani
Martina Contisciani
PhD student

My research focuses on the analysis of network data using statistical tools. My background is in Theoretical and Applied Statistics and I am interested in discovering new techniques, approaches and perspectives used in the analysis of data. I have been working on a project focused on modeling covariate information in community detection algorithms and I am involved in investigating the conditional independence assumption, underlying the statistical inference on network data.

Caterina De Bacco
Caterina De Bacco
CyberValley Research Group Leader

My research focuses on understanding, optimizing and predicting relations between the microscopic and macroscopic properties of complex large-scale interacting systems.