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| Multi-relational Network Identification in Cross-fusion Domains of Information-technology-related Industries |
| LI Baoxiang1, KONG Lingkai2, YANG Xiaomeng3
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1. School of Economics and Management, Xiamen University of Technology, Xiamen 361024, China;
2. Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650504, China;
3. School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 215699, China |
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Abstract In order to identify and analyze the multi-relational network of cross-industry convergence fields, we used the LDA topic model, and applied the topic intensity, text similarity, co-presence rate, co-presence network analysis and the complex network analysis method to identify the multi-relational network. The findings reveal the evolutionary characteristics of multi-relational network topology, maximum communities, and key nodes. The findings indicate that universities have consistently occupied key node positions within these networks and have maintained a dominant control over network resources. Enterprises have gradually taken on more significant node positions in these networks. Furthermore, the core-periphery structures and the phenomenon of enterprises clustering together have become increasingly evident. This research provides a new approach for uncovering the associations and growth patterns among heterogeneous-relation agents in complex networks. It offers empirical evidence for enterprises seeking to acquire technological resources and gain a competitive edge in the market, while also serving as a valuable reference for government departments aiming to optimize policies for industrial convergence and development.
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Published: 15 September 2026
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[1] Chesbrough H W, Vanhaverbeke W, West J, et al. Open Innovation: Researching a New Paradigm[M]. Oxford, UK: Oxford University Press, 2006.
[2] Ravichandran T, Giura S I. Knowledge transfers in alliances: exploring the facilitating role of information technology[J]. Information Systems Research, 2019, 30(3): 726-744.
[3] 马永红, 孔令凯, 林超然, 等. 基于专利挖掘的关键共性技术识别研究[J]. 情报学报, 2020, 39(10): 1093-1103.
Ma Y H, Kong L K, Lin C R, et al. Key generic technology identification based on patent mining[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(10): 1093-1103.
[4] 袁思达. 技术预见德尔菲调查中共性技术课题识别研究[J]. 科学学与科学技术管理, 2009, 30(10): 21-26.
Yuan S D. Recognition of generic technology in technology foresight delphi survey[J]. Science of Science and Management of S & T, 2009, 30(10): 21-26.
[5] 刘波, 常珏宁, 龙彦召. 基于技术预见视角的共性技术筛选实证研究[J]. 科学学与科学技术管理, 2014, 35(10): 26-34.
Liu B, Chang Y N, Long Y Z. Empirical research on generic technology screening based on technology foresight[J]. Science of Science and Management of S & T, 2014, 35(10): 26-34.
[6] 贺正楚, 张蜜, 吴艳, 等. 生物医药产业共性技术服务效率研究[J]. 中国软科学, 2014, 278(2): 130-139.
He Z C, Zhang M, Wu Y, et al. Research on efficiency of service towards generic technology in bio-pharmaceutical industry[J]. China Soft Science, 2014, 278(2): 130-139.
[7] 朱桂龙, 朱明晶, 尹潇. 知识扩散视角下共性技术的商业化评价——基于多层网络的反向识别方法[J]. 科学学与科学技术管理, 2019, 40(2): 16-25.
Zhu G L, Zhu M J, Yin X. Commercialization evaluation of common technology from the perspective of knowledge diffusion: a reverse identification method based on multi-layer network[J]. Science of Science and Management of S & T, 2019, 40(2): 16-25.
[8] 单晓红, 郝秀艳, 刘晓燕. 融合技术社会影响力的关键共性技术识别研究[J]. 情报理论与实践, 2022, 45(11): 115-125,97.
Shan X H, Hao X Y, Liu X Y. Research on the identification of key generic technology integrating the social influence of technology[J]. Information Studies:Theory & Application, 2022, 45(11): 115-125,97.
[9] 王曰芬, 王金树, 关鹏. 主题-主题关联的学科知识网络构建与演化分析[J]. 情报科学, 2018, 36(9): 9-15,102.
Wang R F, Wang J S, Guan P. Research on construction and evolution analysis of discipline knowledge network based on topics association[J]. Information Science, 2018, 36(9): 9-15,102.
[10] 石静, 孙建军. 科技创新团队的知识网络构建与知识测度研究[J]. 情报学报, 2022, 41(9): 900-914.
Shi J, Sun J J. Knowledge network construction and knowledge measurement of the S & T innovation team[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(9): 900-914.
[11] 何劲, 关鹏, 王曰芬. 作者-主题关联的学科知识网络构建与演化分析[J]. 情报科学, 2019, 37(1): 56-62,67.
He J, Guan P, Wang Y F. Research on construction and evolution analysis of discipline knowledge network based on author-topic association[J]. Information Science, 2019, 37(1): 56-62,67.
[12] 孙晓玲, 李冰, 杨阳, 等. 科学知识对技术知识遗传的影响研究——基于多引用代与语义内容视角[J]. 科学学与科学技术管理, 2019, 40(2): 3-15.
Sun X L, Li B, Yang Y, et al. Exploring the influence of scientific knowledge on the inheritance of technical knowledge: based on the analysis of multiple citation generation and semantic content[J]. Science of Science and Management of S & T, 2019, 40(2): 3-15.
[13] 武华维, 王超, 许海云, 等. 知识耦合视角下区域科学—技术—产业协同创新水平的评价方法研究[J]. 情报理论与实践, 2020, 43(5): 91-98,8.
Wu H X, Wang C, Xu H Y, et al. Research on evaluation method of regional science-technology-industry collaborative innovation level from the perspective of knowledge coupling[J]. Information Studies:Theory & Application, 2020, 43(5): 91-98,8.
[14] Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation[J]. The Journal of Machine Learning Research, 2003, 3: 993-1022.
[15] 陈果, 吴微. 细分领域LDA主题分析中选词方案的效果对比研究[J]. 情报理论与实践, 2019, 42(6): 138-143.
Chen G, Wu W. Comparative study on the effect of word selection scheme on the effect of LDA analysis in subdivisions[J]. Information Studies:Theory & Application, 2019, 42(6): 138-143.
[16] 赵蓉英, 戴祎璠, 王旭. 基于LDA模型与ATM模型的学者影响力评价研究——以我国核物理学科为例[J]. 情报科学, 2019, 37(6): 3-9.
Zhao R Y, Dai Y F, Wang X. Scholar influence evaluation research based on LDA model and ATM model—a case study of nuclear physics in China[J]. Information Science, 2019, 37(6): 3-9.
[17] 岳丽欣, 周晓英, 陈旖旎. 期刊论文核心研究主题识别及其演化路径可视化方法研究——以我国医疗健康信息领域期刊论文为例[J]. 图书情报工作, 2020, 64(5): 89-99.
Yue L X, Zhou X Y, Chen Y N. Research on topic identification of papers core research subjects and evolution path visualization method—taking China’s journal of medical and health information as an example[J]. Library and Information Service, 2020, 64(5): 89-99.
[18] 张鑫, 文奕, 许海云, 等. Prophet预测-修正的主题强度演化模型——以干细胞领域为实证[J]. 图书情报工作, 2020, 64(8): 78-92.
Zhang X, Wen Y, Xu H Y, et al. Prophet prediction-correction topic evolution model—a case study in stem cell field[J]. Library and Information Service, 2020, 64(8): 78-92.
[19] 栾春娟. 战略性新兴产业共性技术测度指标实证研究[J]. 中国科技论坛, 2012, 194(6): 73-77.
Luan C J. Emperical study on the measuring indicators of generic technology of emerging industries of strategic importance[J]. Forum on Science and Technology in China, 2020, 64(8): 78-92. |
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