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| Predicting Potential Technology Partners and Competitors of Enterprises: A Case Study on Fuel Cell Technology |
| Li Bing, Ding Kun, Sun Xiaoling |
| Institute of Science of Science and S&T Management, Dalian University of Technology, Dalian 116024 |
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Abstract The rapid increase in the number of patents has made it more time-consuming and labor-intensive for companies to evaluate and screen potential technology partners and identify and avoid competitors in a large industry. The method of accurately and quickly narrowing the search scope and locating potential relationships becomes crucial and meaningful. In this study, a company-patent heterogeneous network is constructed based on the bipartite graph theory. The research method uses the link prediction algorithm based on the SimRank indicator of random walk, and a predictive analysis is conducted on the company's potential technology partners and competitors. The network representation method is used to identify the context information of patents and calculate the similarity of the patent representation vector to measure the technical difference between the target company and the competing object to determine the competitive relationship. Finally, an empirical study in the field of fuel cell technology is carried out to verify the effectiveness of the research theory and method and provide a reference for the development of enterprises.
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Received: 26 September 2020
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1 洪勇, 李英敏. 基于专利耦合的企业间技术相似性可视化研究[J]. 科学学研究, 2013, 31(7): 1013-1021.
2 王菲菲, 芦婉昭, 贾晨冉, 等. 基于论文-专利机构合作网络的产学研潜在合作机会研究[J]. 情报科学, 2019, 37(9): 9-16.
3 许海云, 王超, 董坤, 等. 基于创新链中知识溢出效应的产学研R&D合作对象识别方法研究[J]. 情报学报, 2017, 36(7): 682-694.
4 王超, 许海云, 方曙. 产学研潜在合作对象识别方法研究[J]. 科学学研究, 2018, 36(1): 101-113.
5 周超强. 面向产学研合作的专家推荐方法研究[D]. 杭州: 杭州电子科技大学, 2016.
6 付鑫金, 朱礼军, 曹燕, 等. 科技查新用于识别产学研潜在合作对象的方法研究[J]. 情报工程, 2018, 4(2): 101-111.
7 冉从敬, 宋凯, 赵倩蓉, 等. 合作什么, 去哪合作, 与谁合作?——专利视角下的校企合作对象选择系统构建[J]. 图书馆论坛, 2020, 40(8): 113-121.
8 Kang J, Lee J, Jang D, et al. A methodology of partner selection for sustainable industry-university cooperation based on LDA topic model[J]. Sustainability, 2019, 11(12): 3478.
9 温芳芳. 专利分类号耦合分析在企业潜在合作关系识别中的应用[J]. 现代情报, 2018, 38(7): 142-147.
10 杨梓. 基于技术关联的企业技术创新合作对象识别与选择研究[D]. 北京: 北京工业大学, 2016.
11 傅俊英, 彭喆, 郑佳, 等. 基于专利异构网络的中小企业潜在合作伙伴研究——以石墨烯领域为例[J]. 情报学报, 2019, 38(4): 391-401.
12 温亮, 邱鹏君, 马萍萍, 等. 基于SAO语义分析的潜在技术合作伙伴识别[J]. 北京理工大学学报(社会科学版), 2017, 19(4): 91-96.
13 Angue K, Ayerbe C, Mitkova L. A method using two dimensions of the patent classification for measuring the technological proximity: an application in identifying a potential R&D partner in biotechnology[J]. The Journal of Technology Transfer, 2014, 39(5): 716-747.
14 丁勇, 程璐, 蒋翠清. 基于二部图的P2P网络借贷投资组合决策方法[J]. 数据分析与知识发现, 2019, 3(12): 76-83.
15 任辉, 周晓光, 申晋. 基于二部图法的Web知识社群挖掘[J]. 现代图书情报技术, 2007(4): 39-42.
16 张继东, 王蓉. 基于用户行为感知的数字期刊服务推送研究[J]. 情报科学, 2019, 37(5): 19-24.
17 Aaldering L J, Leker J, Song C H. Competition or collaboration? Analysis of technological knowledge ecosystem within the field of alternative powertrain systems: a patent-based approach[J]. Journal of Cleaner Production, 2019, 212: 362-371.
18 Park I, Yoon B. Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network[J]. Journal of Informetrics, 2018, 12(4): 1199-1222.
19 王宏起, 夏凡, 王珊珊. 新兴产业技术融合方向预测:方法及实证[J]. 科学学研究, 2020, 38(6): 1009-1017, 1075.
20 吕琳媛. 复杂网络链路预测[J]. 电子科技大学学报, 2010, 39(5): 651-661.
21 张斌, 李亚婷. 学科合作网络链路预测结果的排序鲁棒性[J]. 信息资源管理学报, 2018, 8(4): 89-97.
22 Antonellis I, Garcia-Molina H, Chang C C. Simrank++: query rewriting through link analysis of the click graph[J]. Proceedings of the VLDB Endowment, 2008, 1(1): 408-421.
23 林原, 王凯巧, 刘海峰, 等. 网络表示学习在学者科研合作预测中的应用研究[J]. 情报学报, 2020, 39(4): 367-373.
24 孙晓玲, 丁堃. 深度学习中的表示学习研究及其对知识计量的影响[J]. 情报理论与实践, 2018, 41(9): 118-122. |
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