Exploring knowledge flow within a technology domain by conducting a dynamic analysis of a patent co-citation network

Abstract

Purpose This paper aims to present a methodology by which future knowledge flow can be predicted by predicting co-citations of patents within a technology domain using a link prediction algorithm applied to a co-citation network. Design/methodology/approach Several methods and approaches are used: a dynamic analysis of a patent citation network to identify technology life cycle phases, patent co-citation network mapping from the patent citation network and the application of link prediction algorithms to the patent co-citation network. Findings The results of the presented study indicate that future knowledge flow within a technology domain can be predicted by predicting patent co-citations using the preferential attachment link prediction algorithm. Furthermore, they indicate that the patent – co-citations occurring between the end of the growth life cycle phase and the start of the maturation life cycle phase contribute the most to the precision of the knowledge flow prediction. Finally, it is demonstrated that most of the predicted knowledge flow occurs in a time period closely following the application of the link – prediction algorithm. Practical implications By having insight into future potential co-citations of patents, a firm can leverage its existing patent portfolio or asses the acquisition value of patents or the companies owning them. Originality/value It is demonstrated that the flow of knowledge in patent co-citation networks follows a rich get richer intuition. Moreover, it is show that the knowledge contained in younger patents has a greater chance of being cited again. Finally, it is demonstrated that these co-citations can be predicted in the short term when the preferential attachment algorithm is applied to a patent co-citation network.

Sustainable Human Resource Management, Green Human Resource Management, and Environmental Human Resource Management: Analysis of New and Emerging Terms Related to the Human Resource Area

“…Strong co-citation links indicate the similarity of subject matter (Small, 1973). Today, co-citation analysis is used to investigate the structure of research collaboration (Chen, Zhang and Fu, 2019), discover the journals that formed the intellectual base of a knowledge domain (Hu et al, 2018), to predict knowledge flows (Smojver, Štorga and Zovak, 2021), or to identify emerging trends (Liu, Jiang and Ma, 2013;Ruan et al, 2019).…”

Spatial-temporal evolution of technology flows in China’s Beijing-Tianjin-Hebei region: Patent transfer networks 2003–2021

“…Giglio et al (2021) [ 42 ] propose a method to study cross-country creativity/knowledge flows by analyzing patent citation networks using the aircraft, aviation and aerospace (AAC) industry as a case study. Smojver et al (2021) [ 43 ] identify technology life cycle by performing a dynamic analysis of patent citation networks to stages, mapping patent co-citation networks from patent citation networks, and applying link prediction algorithms to patent co-citation networks to explore knowledge flows within technology domains.…”