Knowledge Diffusion Characteristics of Highly Disruptive Patents Based on Citation Network
Pan Yiru,Mao Jin,Li Gang()
School of Information Management, Wuhan University, Wuhan 430072, China Center for the Studies of Information Resources, Wuhan University, Wuhan 430072, China
[Objective] This paper explores the knowledge diffusion of disruptive patents. [Methods] First, we used the disruption index to identify highly disruptive patents from the USPTO database. Then, we matched the patents of the control group according to the number of citations and co-citation couplings. Third, we analyzed the knowledge diffusion characteristics of highly disruptive patents from citation distribution and citation network characteristics. Finally, we built a regression model to reveal the core features. [Results] The citation take-off point of highly disruptive patents appeared 1 to 3 years after authorization. The increasing speed peaked in 3 to 5 years and decreased from the 6th year. Significant differences exist between highly disruptive and control group patents in knowledge diffusion intensity, efficiency, local and global knowledge diffusion capabilities, etc. First citation-year, first-peak interval-year, and first-peak-year citation metrics, average path length, average clustering coefficient, and connectivity metrics for low-citation generations help us identify highly disruptive patents. [Limitations] The disruption index fluctuates over time. This study selects highly disruptive patents according to the time interval, and its disruption index value is not yet stable. [Conclusions] The study reveals the knowledge diffusion characteristics of disruptive technologies from the perspective of patent citations and provides theoretical support for identifying disruptive technologies.
潘一如, 毛进, 李纲. 基于引文网络的高颠覆性专利知识扩散特征研究*[J]. 数据分析与知识发现, 2023, 7(10): 1-14.
Pan Yiru, Mao Jin, Li Gang. Knowledge Diffusion Characteristics of Highly Disruptive Patents Based on Citation Network. Data Analysis and Knowledge Discovery, 2023, 7(10): 1-14.
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