The patent is one of the carriers of scientific research and development, and an indicator of technical innovation. As a promising approach for modeling complex systems, complex networks could provide the sound theoretical framework for developing proper simulation models. Many researchers use the relations of patent citations and transfers to study knowledge propagate and output in the network. However, knowledge flow in patents should be fully considered by substantial and fruitful connections, both in the process of knowledge application and knowledge output. In this paper, we present a two-boundary network model with knowledge application boundary and knowledge output boundary to reveal the patent citation patterns in knowledge flow. The feasibility and effectiveness of the two-boundary network model are proved with theory and experiment. Utilizing 578,678 patents from the United States Patent and Trademark Office between 2015 and 2018 with the two-boundary network model, we put up a fixed effect ordinary least square equation to reveal the patent impacts of different structural patterns. Experimental results show that, in the perspective of structural patterns, the highly impacted patents without assigning are greatly influenced by their scientific literature references and provide knowledge for other assigned patents. However, considering all the fixed effect factors, patents that transfer knowledge from other patents to assigned patents are more likely to become highly impacted patents. Besides, we find the two-boundary network model fits the real patent knowledge flow well by comparing it with the other models. INDEX TERMS Two-boundary network model, knowledge flow, regression equation, fixed effect.
With the development of worldwide knowledge-based economy, structures of knowledge diffusion in scientific research have become extremely complex and dynamic. Properly evaluating the knowledge diffusion would encourage authors to pursue high quality researches. Hence, this paper presents a novel metric of independency of knowledge diffusion (IKD) on the published paper v, defined as the ratio of citation counts of v without its references’ to citation counts of v and its references’ minus their commons’. Utilizing the inverse citation network formed by published papers in American Physical Society (APS) from 1997 and 2016, the experimental results show that the distributions of IKD are following power law behaviors and the values of IKD are affected by citation counts and involved cooperative institutions. It is reasonable to assess the performances of knowledge diffusion by the metric of IKD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.