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首页> 外文期刊>The Journal of grey system >Study on Optimization of CoUaborative Innovation Network Structures Based on Grey Property of Knowledge Flows
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Study on Optimization of CoUaborative Innovation Network Structures Based on Grey Property of Knowledge Flows

机译：基于知识流灰色特性的协同创新网络结构优化研究

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摘要

This paper establishes a model of collaborative innovation based on grey system theory and explores how the efficiency of innovation is influenced by the structures of collaborative innovation networks. The result shows that when the degree of grayscale of knowledge flow is low, the optimistic collaborative innovation network structure is random network which is characterized by low average path length; when the degree of grayscale of knowledge flow is medium, the optimistic collaborative innovation network structure is small world network which is characterized by high small-world coefficient; when the degree of grayscale of knowledge flow is high, the optimistic collaborative innovation network structure is regular network which is characterized by high average clustering coefficient. Two reasons of the results are derived. First, when the degree of grayscale of knowledge is low, it is easy for innovators to obtain certain kinds of knowledge, so expanding knowledge variety becomes more important for innovation; on the contrary, obtaining certain kinds of knowledge is more important for innovation. Second, high average clustering coefficient of the network is good for obtaining certain kinds of knowledge; low average path length is good for the diversification of knowledge. At last, some advices are proposed.
机译：本文建立了基于灰色系统理论的协同创新模型，并探讨了协同创新网络结构如何影响创新效率。结果表明，当知识流的灰度等级较低时，乐观的协同创新网络结构为随机网络，其平均路径长度较低;当知识流的灰度等级为中等时，乐观的协同创新网络结构为小世界网络，其特点是小世界系数高。当知识流的灰度级高时，乐观的协同创新网络结构是规则网络，其特征是平均聚类系数高。得出结果的两个原因。首先，当知识的灰度等级较低时，创新者很容易获得某些种类的知识，因此扩大知识种类对于创新就变得更加重要。相反，获得某些种类的知识对于创新更重要。其次，较高的网络平均聚类系数有利于获得某些知识。平均路径长度低有利于知识的多样化。最后，提出了一些建议。

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