基于知识-合作相依网络的技术融合预测与潜在合作伙伴识别研究

赵健宇, 董振杰, 余乐安, 袭希, 姚欣林

系统工程理论与实践 ›› 2025, Vol. 45 ›› Issue (3) : 996-1013.

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系统工程理论与实践 ›› 2025, Vol. 45 ›› Issue (3) : 996-1013. DOI: 10.12011/SETP2023-2079
论文

基于知识-合作相依网络的技术融合预测与潜在合作伙伴识别研究

    赵健宇1, 董振杰1, 余乐安2, 袭希3, 姚欣林4
作者信息 +

Research on the forecasting of technological convergence and identification of potential cooperative partners based on the knowledge-collaboration interdepend networks

    ZHAO Jianyu1, DONG Zhenjie1, YU Lean2, XI Xi3, YAO Xinlin4
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摘要

由于以技术融合为新代表的创新活动双重嵌入在知识-合作相依网络中, 因此精准预见知识网络中知识元素可能的组合关系, 并据此助力合作网络中创新主体准确识别潜在的合作伙伴, 对于高质量实践跨学科、跨领域技术全面融合的创新战略具有重要意义.为完整挖掘相依网络既有信息, 同时弥补现有成果无法同时表示网络空间和时间特征的研究缺陷, 采用自编码器结合Transformer深度学习算法的方式, 提出动态知识网络链路预测模型E-Transformer-D, 构建面向技术融合的知识-合作相依网络动态链路预测框架.在利用8位国际专利分类代码对知识元素予以更精细化表征的基础上, 借助医疗器械领域的专利数据集对模型予以验证.结果表明, 基于Transformer改进的知识-合作相依网络动态链路预测框架不仅可以更高精度地预测知识元素的融合方向, 而且能够更具针对性地为合作网络中的创新主体识别潜在的合作伙伴提供有力证据.研究方法与结论既能够在理论层面上丰富技术融合领域的研究内容, 又可以实践层面上为创新主体提升技术融合成功率提供科学参考.

Abstract

Since the innovation activities are characterized by technological convergence dual embedded in the knowledge-collaboration networks, it has great significance to practice interdisciplinary technological convergence with high quality by accurately foreseeing the combination relationships among knowledge elements in knowledge networks and further contributing to innovation subjects to precisely recognizing the latent partners in collaboration networks. In order to completely mine the existing information of interdependent networks while addressing the gaps in prior studies that fail to represent the spatial and temporal characteristics of networks at the same time, this study uses the method of combing self-encoder and algorithm of deep learning of Transformer to propose the dynamic knowledge network link prediction model E-Transformer-D, and construct a knowledge-collaboration interdependent network dynamic link prediction framework oriented to the technology convergence. On the basis of manifesting the knowledge element via 8-bit international patent classification code, we employ the patent set data in the medical device field to verify the usefulness and precision of our model. Results indicate that the dynamic link prediction algorithm based on the improved Transformer can only more precisely forecast the convergence direction of knowledge elements but also be more targeted to provide evidence to identify the potential partners in collaboration networks, simultaneously. The research algorithm and conclusions can enrich the contents of the technological convergence area at the theoretical level, as well as offer scientific references for innovation subjects to enhance the successful rate of technological convergence at the practical level.

关键词

技术融合 / 知识-合作相依网络 / 动态链路预测 / 合作伙伴识别 / 知识元素组合

Key words

technological convergence / knowledge-collaboration interdepend networks / dynamic link prediction / cooperative partners identification / knowledge elements combinations

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赵健宇 , 董振杰 , 余乐安 , 袭希 , 姚欣林. 基于知识-合作相依网络的技术融合预测与潜在合作伙伴识别研究. 系统工程理论与实践, 2025, 45(3): 996-1013 https://doi.org/10.12011/SETP2023-2079
ZHAO Jianyu , DONG Zhenjie , YU Lean , XI Xi , YAO Xinlin. Research on the forecasting of technological convergence and identification of potential cooperative partners based on the knowledge-collaboration interdepend networks. Systems Engineering - Theory & Practice, 2025, 45(3): 996-1013 https://doi.org/10.12011/SETP2023-2079
中图分类号： C931   

参考文献

[1] Kim K, Jung S, Hwang J. Technology convergence capability and firm innovation in the manufacturing sector: An approach based on patent network analysis[J]. R&D Management, 2019, 49(4): 595-606.
[2] 科技部, 中央宣传部, 中国科协. "十四五" 国家科学技术普及发展规划 [EB/OL]. [2024-04-03]. https://www.gov.cn/zhengce/zhengceku/2022-08/16/content_5705580.html Ministry of Science and Technology, Publicity Department of the Central Committee of the Communist Party of China, China Association for Science and Technology. "14th Five-Year" National Science and Technology Popularization Development Plan[EB/OL]. [2024-04-03]. https://www.gov.cn/zhengce/zhengceku/2022-08/16/content_5705580.html
[3] 张华, 顾新. 合作创新的领导权博弈与利益协调研究 [J]. 系统工程理论与实践, 2018, 38(12): 3109-3123. Zhang H, Gu X. Leadership game and benefit coordination in cooperative innovation[J]. Systems Engineering—Theory & Practice, 2018, 38(12): 3109-3123.
[4] Islam N, Miyazaki K. Nanotechnology innovation system: Understanding hidden dynamics of nanoscience fusion trajectories[J]. Technological Forecasting and Social Change, 2009, 76(1): 128-140.
[5] Hacklin F, Wallin M W, Björkdahl J, et al. The making of convergence: Knowledge reuse, boundary spanning, and the formation of the ICT industry[J]. IEEE Transactions on Engineering Management, 2021(99): 1-13.
[6] Xi X, Ren F F, Yu L A, et al. Detecting the technology's evolutionary pathway using Hids-trait-driven tech mining strategy[J]. Technological Forecasting and Social Change, 2023, 195: 122777.
[7] Karvonen M, Kässi T. Patent citations as a tool for analysing the early stages of convergence[J]. Technological Forecasting and Social Change, 2013, 80(6): 1094-1107.
[8] Bergh D D, Ketchen J D J, Orlandi I. Information asymmetry in management research: Past accomplishments and future opportunities[J]. Journal of Management, 2019, 45(1): 122-158.
[9] 韩菁, 唐箫, 余乐安. 基于多层网络链路预测的潜在合作关系识别研究 [J]. 系统工程理论与实践, 2021, 41(4): 1049-1060. Han J, Tang X, Yu L A. Research on identification of potential partnership based on link prediction with multilayer networks[J]. Systems Engineering—Theory & Practice, 2021, 41(4): 1049-1060.
[10] Carnabuci G, Bruggeman J. Knowledge specialization, knowledge brokerage and the uneven growth of technology domains[J]. Social Forces, 2009, 88(2): 607-641.
[11] Wang C, Rodan S, Fruin M, et al. Knowledge networks, collaboration networks, and exploratory innovation[J]. Academy of Management Journal, 2014, 57(2): 484-514.
[12] 刘晓燕, 张淑伟, 单晓红. 面向技术融合的合作伙伴识别研究 [J/]. 科学学研究, 2023, 41(12): 2164-2174. Liu X Y, Zhang S W, Shan X H. Research on partner identification for technology convergence: Base on multi-layer network link prediction[J]. Studies in Science of Science, 2023, 41(12): 2164-2174.
[13] Song B, Seol H, Park Y. A patent portfolio-based approach for assessing potential R&D partners: An application of the Shapley value[J]. Technological Forecasting and Social Change, 2016, 103: 156-165.
[14] Lü L, Zhou T. Link prediction in complex networks: A survey[J]. Physica A: Statistical Mechanics and Its Applications, 2011, 390(6): 1150-1170.
[15] Yayavaram S, Ahuja G. Decomposability in knowledge structures and its impact on the usefulness of inventions and knowledge-base malleability[J]. Administrative Science Quarterly, 2008, 53(2): 333-362.
[16] Singh J. Collaborative networks as determinants of knowledge diffusion patterns[J]. Management Science, 2005, 51(5): 756-770.
[17] Guan J C, Liu N. Exploitative and exploratory innovations in knowledge network and collaboration network: A patent analysis in the technological field of nano-energy[J]. Research Policy, 2016, 45(1): 97-112.
[18] Brennecke J, Rank O. The firm's knowledge network and the transfer of advice among corporate inventors—A multilevel network study[J]. Research Policy, 2017, 46(4): 11787.
[19] Lü L, Jin C H, Zhou T. Similarity index based on local paths for link prediction of complex networks[J]. Physical Review E, 2009, 80(4): 046122.
[20] 冯译萱, 张月霞. 一种时序有向网络中的链路预测方法 [J]. 计算机工程与应用, 2019, 55(21): 151-157. Feng Y X, Zhang Y X. Link prediction methods in directed neteork[J]. Computer Engineering and Applications, 2019, 55(21): 151-157.
[21] 张元钧, 张曦煌. 基于图卷积与长短期记忆网络的动态网络表示学习模型 [J]. 计算机应用, 2021, 41(7): 1857-1864. Zhang Y J, Zhang X H. Dynamic network representation learning model based on graph convolutional network and long short-term memory network[J]. Journal of Computer Applications, 2021, 41(7): 1857-1864.
[22] 江若然, 张玲玲. 社交属性网下基于链路预测及节点度的推荐算法 [J]. 管理评论, 2019, 31(2): 119-129. Jiang R R, Zhang L L. Recommendation algorithm based on link prediction and node degree using a social-attribute network[J]. Business Review, 2019, 31(2): 119-129.
[23] Liben-Nowell D, Kleinberg J. The link prediction problem for social networks[C]// Proceedings of the Twelfth International Conference on Information and Knowledge Management, 2003: 556-559.
[24] Zhang J, Zhu L. Citation recommendation using semantic representation of cited papers' relations and content[J]. Expert Systems with Applications, 2022, 187: 115826.
[25] Hu G, Lu G, Zhao Y. FSS-GCN: A graph convolutional networks with fusion of semantic and structure for emotion cause analysis[J]. Knowledge-based Systems, 2021, 212: 106584.
[26] Li S, Jin X, Xuan Y, et al. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting[J]. Advances in Neural Information Processing Systems, 2019, 32: 1-11.
[27] Belenzon S, Patacconi A. Innovation and firm value: An investigation of the changing role of patents, 1985-2007[J]. Research Policy, 2013, 42(8): 1496-1510.
[28] Dangelico R M, Garavelli A C, Petruzzelli A M. A system dynamics model to analyze technology districts' evolution in a knowledge-based perspective[J]. Technovation, 2010, 30(2): 142-153.
[29] Kuhn T S. The structure of scientific revolution[M]. Chicago: University of Chicago Press, 1996.
[30] Jaffe A B, De Rassenfosse G . Patent citation data in social science research: Overview and best practices[J]. Journal of the Association for Information Science and Technology, 2017, 68(6): 1360-1374.
[31] Doménech T, Davies M. The role of embeddedness in industrial symbiosis networks: Phases in the evolution of industrial symbiosis networks[J]. Business Strategy and the Environment, 2010, 20(5): 281-296.
[32] Carnabuci G, Operti E. Where do firms' recombinant capabilities come from? Intraorganizational networks, knowledge, and firms' ability to innovate through technological recombination[J]. Strategic Management Journal, 2013, 34(13): 1591-1613.
[33] Fleming L, Mingo S, Chen D. Collaborative brokerage, generative creativity, and creative success[J]. Administrative Science Quarterly, 2007, 52(3): 443-475.
[34] Vom Stein N, Sick N, Leker J. How to measure technological distance in collaborations—The case of electric mobility[J]. Technological Forecasting and Social Change, 2015, 97: 154-167.
[35] Yayavaram S, Ahuja G. Decomposability in knowledge structures and its impact on the usefulness of inventions and knowledge-base malleability[J]. Administrative Science Quarterly, 2008, 53(2): 333-362.
[36] Grigoriou K, Rothaermel F T. Organizing for knowledge generation: Internal knowledge networks and the contingent effect of external knowledge sourcing[J]. Strategic Management Journal, 2017, 38: 395-414.
[37] Lee C, Kogler D F, Lee D. Capturing information on technology convergence, international collaboration, and knowledge flow from patent documents: A case of information and communication technology[J]. Information Processing & Management, 2019, 56(4): 1576-1591.
[38] Liu N, Mao J, Guan J. Knowledge convergence and organization innovation: The moderating role of relational embeddedness[J]. Scientometrics, 2020, 125(3): 1899-1921.
[39] Zhao J Y, Bai A Z, Xi X, et al. Impacts of malicious attacks on robustness of knowledge networks: A multi-agent-based simulation[J]. Journal of Knowledge Management, 2020, 24(5): 1079-1106.
[40] Ho J Y, O'Sullivan E. Toward integrated innovation roadmapping: Lessons from multiple functional roadmaps beyond technology R&D[J]. IEEE Transactions on Engineering Management, 2020, 69(1): 155-167.
[41] Wang Q R, Zheng Y. Patent regime and the geography of cumulative innovation[J]. Research Policy, 2023, 52(7): 104809.
[42] 寇宗来, 刘学悦. 中国企业的专利行为: 特征事实以及来自创新政策的影响 [J]. 经济研究, 2020, 55(3): 83-99. Kou Z L, Liu X Y. On patenting behavior of chinses firm: Stylized facts and effects of innovation policy[J]. Economic Research Journal, 2020, 55(3): 83-99.
[43] Hong S, Lee C. Effective indexes and classification algorithms for supervised link prediction approach to anticipating technology convergence: A comparative study[J]. IEEE Transactions on Engineering Management, 2021, 10: 1-12.
[44] Pham P, Nguyen L T, Nguyen N T, et al. ComGCN: Community-driven graph convolutional network for link prediction in dynamic networks[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 52(9): 5481-5493.
[45] Chen J, Zhang J, Xu X, et al. E-LSTM-D: A deep learning framework for dynamic network link prediction[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 51(6): 3699-3712.
[46] Goyal P, Chhetri S R, Canedo A M. Dyngraph2vec: Capturing network dynamics using dynamic graph representation learning[J]. Knowledge-Based System, 2020, 16: 550-771.
[47] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017, 30: 5998-6008.
[48] Carnabuci G, Operti E. Where do firms' recombinant capabilities come from? Intraorganizational networks, knowledge, and firms' ability to innovate through technological recombination[J]. Strategic Management Journal, 2013, 34(13): 1591-1613.
[49] Caviggioli F. Technology fusion: Identification and analysis of the drivers of technology convergence using patent data[J]. Technovation, 2016, 55: 22-32.
[50] Cho J H, Lee J, Sohn S Y. Predicting future technological convergence patterns based on machine learning using link prediction[J]. Scientometrics, 2021, 126(7): 5413-5429.
[51] San K T, Sohn S Y. Machine-learning-based deep semantic analysis approach for forecasting new technology convergence[J]. Technological Forecasting and Social Change, 2020, 157: 1-10.
[52] Li Z L, Ge Y, Bai X. What will be popular next? Predicting hotspots in two-mode social networks[J]. Management Information Systems Quarterly, 2020: 45(2): 925-966.
[53] London B, Huang B, Getoor L. Stability and generalization in structured prediction[J]. The Journal of Machine Learning Research, 2016, 17(1): 7808-7859.
[54] Mohan A, Venkatesan R, Pramod K V. A scalable method for link prediction in large real world networks[J]. Journal of Parallel and Distributed Computing, 2017: 109: 89-101.
[55] Vallittu P K, Närhi T O, Hupa L. Fiber glass-bioactive glass composite for bone replacing and bone anchoring implants[J]. Dental Materials, 2015, 31(4): 371-381.

基金

国家自然科学基金(72072046);国家自然科学基金重点项目(72331007);黑龙江省社会科学项目(21GLB060);黑龙江省自然科学基金(LH2022G004)
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