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摘要: 通过对中国医药制造业产学研合作创新网络进行复杂网络分析,探究网络的演化及小世界性对合作创新网络创新绩效的影响。首先通过复杂网络分析得出中国医药制造业产学研合作创新网络具有无标度网络特性,且上海医药制造业的6个凝聚子群之间的开放度和交流频率有利于创新的形成和传播;其次运用QAP回归分析得出小世界性对上海市产学研合作创新网络的创新绩效会产生一定不利影响。最后通过以上分析提出相应的对策建议。关键词: 产学研合作创新网络;医药制造业;复杂网络;小世界性;拓扑演化基金资助:国家社科基金青年项目(18CGL015)![]()
Research on Complex Networks Based on the Topological Evolution of Industry-University-Research Cooperative Innovation Networks
WU Huia, GU Xiaomina, ZHAO Yuanjunb
a. School of Financial Technology; b. School of Business Administration, Shanghai Lixin University of Accounting and FinanceAbstract：This study is devoted to the complex network analysis of China pharmaceutical manufacturing industry-university-research cooperative innovation network, to explore the evolution of the network and the impact of small world on the innovation performance of cooperative innovation network. Firstly, through complex network analysis, this study concludes that China′s pharmaceutical manufacturing industry-university-research cooperative innovation network has scale-free network characteristics, and the openness and communication frequency between the six cohesive subgroups of Shanghai pharmaceutical manufacturing industry are conducive to the formation of innovation and Communication; Secondly, using QAP regression analysis, this paper concludes that the small world has a certain adverse impact on the innovation performance of the Shanghai Industry-University-Research Cooperation Innovation Network. Finally, corresponding countermeasures and suggestions are put forward according to above analysis results.Keywords：cooperative innovation networks of
industry-university-research institution；pharmaceutical manufacturing industry；complex network； feature of
small-world；topological evolution作者介绍:
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第一作者：吴慧(1990-),女,河南潢川人,博士,讲师,主要研究方向为合作网络、金融科技、创新绩效。
引用本文:
吴慧, 顾晓敏, 赵袁军. 产学研合作创新网络拓扑演化的复杂网络研究[J]. 复杂系统与复杂性科学, 2020, 17(4): 38-47.
WU Hui, GU Xiaomin, ZHAO Yuanjun. Research on Complex Networks Based on the Topological Evolution of Industry-University-Research Cooperative Innovation Networks. Complex Systems and Complexity Science, 2020, 17(4): 38-47.
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