Robust Networks, Pivotal Patents: Identifying and Assessing Key Technological Influencers

Abstract

In a world of swiftly changing technology and external challenges, predicting the role of core patents in technology systems' strength and power is vital. This research presents a method that combines robustness analysis of patent citation networks with core patent identification, assessing their global industrial technology innovation significance. It aims to identify patents key to network stability and external change adaptation, understanding their impact in dynamic tech environments. Using network robustness, the study examines connectivity, efficiency, and clustering in patent citation networks, assessing patent node importance based on structural feature changes postremoval. The study employs patents from five technological domains as case studies, ranking the importance of nodes and exploring how patent attributes affect these rankings. This research contributes by merging patent network robustness with valuation, supporting IP strategies and tech management policies, and offering insights into tech system complexity and dynamism.