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Early Identification of Star Inventor Types in the Perspective of Innovation Duality |
Liu Xiang(),Liu Xiang,Yu Bowen |
School of Information Management, Central China Normal University, Wuhan 430079, China |
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Abstract [Objective] Identifying the star inventors by the number of patents and patent citations has obvious time lag effects. Therefore, this paper constructs a graph convolutional neural network to find the emerging star inventors effectively. [Methods] This paper defines four types of star inventors: “composite”, “consolidation”, “breakthrough” and “development” which can also be grouped as “continuity innovation” and “breakthrough innovation”. Then, we constructed a model based on graph convolutional neural network combining patent titles and the cooperation relationship to find star inventors. [Results] We examined our model with patent data in the field of molecular biology and microbiology. The overall accuracy of this model in identifying the innovation types of star inventors reached 79.4%, which was about 15% higher than the method using word vectors. [Limitations] The proposed model could not identify “breakthrough star inventors” effectively. [Conclusions] Our new model could reduce the time-lag effect of the existing methods and identify the innovation type of star inventors earlier.
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Received: 11 April 2022
Published: 28 March 2023
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Fund:National Natural Science Foundation of China(71673106) |
Corresponding Authors:
Liu Xiang,ORCID:0000-0003-4315-2699,E-mail: xiangliu@mail.ccnu.edu.cn。
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