基于动态图卷积网络的发明人潜在合作伙伴识别方法研究  

Identifying Potential Partners of Inventors Based on Dynamic Graph Convolutional Networks

作　　者：谢小东 吴洁[1] 盛永祥[1] 王建刚[1] 周潇[1] Xie Xiaodong;Wu Jie;Sheng Yongxiang;Wang Jiangang;Zhou Xiao(School of Economics and Management,Jiangsu University of Science and Technology,Zhenjiang 212100,China)

机构地区：[1]江苏科技大学经济管理学院,镇江212100

出　　处：《数据分析与知识发现》2025年第3期83-95,共13页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金面上项目(项目编号:72171122);江苏省研究生科研与实践创新计划项目(项目编号:KYCX23_3817)的研究成果之一。

摘　　要：【目的】为促进发明人之间的合作交流,提高创新效率,本文重点研究如何帮助发明人从海量专利文献中识别潜在合作伙伴及辨析合作伙伴类型。【方法】从动态网络视角考虑发明人合作网络结构和节点属性的动态变化,提出基于动态图卷积网络的发明人潜在合作伙伴识别方法,进而细分发明人合作伙伴类型。【结果】采用集成电路领域专利数据进行实证,本文方法AUC值达到0.8464,错误率为0.2897,ER+为0.0830,ER-为0.2067,均显著优于基线模型。【局限】仅考虑了发明人的专利信息,忽略了发明人的多源创新成果,如论文信息等。【结论】本文方法利用合作网络结构及节点属性随时间的动态变化,在进行伙伴识别时能够有效提升模型准确率。通过识别潜在合作伙伴及细分伙伴类型,有助于发明人选择恰当的合作策略,提高合作效率和效果,有效补充了现有合作伙伴选择方法研究框架。[Objective]To promote collaboration among inventors and enhance innovation efficiency,this study identifies potential collaborators and distinguishes their types from patent documents.[Methods]From a dynamic network perspective,this study considered the structural and attribute changes of the inventor collaboration network over time.We proposed a method of identifying potential collaborators for inventors based on dynamic graph convolutional networks,allowing for further classification of inventor partners.[Results]Utilizing patent data from the integrated circuit sector for empirical validation,the proposed method achieved an AUC of 0.8464,an error rate of 0.2897,ER+of 0.0830,and ER-of 0.2067.All of them significantly outperformed baseline models.[Limitations]The study only considers inventors’patent information while ignoring other multi-source innovation outputs,such as academic papers.[Conclusions]The proposed method effectively enhances the accuracy of potential partner identification by leveraging dynamic changes in network structures and node attributes.Identifying potential collaborators and categorizing their types help inventors formulate collaboration strategies,enhancing efficiency and outcomes.This study effectively supplements the existing frameworks for partner selection methodologies.

关 键 词：动态图卷积网络 发明人 合作网络 节点属性 合作伙伴 伙伴类型 

分 类 号：F273[经济管理—企业管理] G350[经济管理—国民经济]