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Modeling the evolution of collaboration network and knowledge network and their effects on knowledge flow through social network analysis

机译:通过社交网络分析建模协作网络和知识网络的演化及其对知识流的影响

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摘要

As important channels of information transmission and knowledge flow, collaboration network and knowledge network can aid network actors in information update and knowledge creation. However, only a few comparative analyses have been conducted on knowledge flow in these two networks. To identify the knowledge flow in collaboration network and knowledge network, this study deployed a database of nanometer photoelectron patents taken out by China at the State Intellectual Property Office over the period 2000 to 2015, and analyzed the evolution of these two networks in terms of network typology, node centrality and core-periphery structure. Results indicate that collaboration network and knowledge network are decoupling and developing in negative directions. Specifically, the collaboration networks of nanometer photoelectron patents decrease in scale, which hinders information transmission, whereas the knowledge networks increase in scale, which is beneficial to knowledge flow. In addition, the degree centrality and betweenness centrality of most nodes in collaboration network and knowledge network are positively related. Universities and ultra-micro techniques maintain core positions in the evolution of collaboration network and knowledge network and exert significant effects on knowledge flow. This study offers a viable method of knowledge flow identification based on the joint investigation of knowledge network and collaboration network and sheds light on efficiency enhancement of knowledge management within these two networks.
机译:作为信息传输和知识流的重要渠道,协作网络和知识网络可以帮助网络参与者进行信息更新和知识创造。但是,对这两个网络中的知识流只进行了很少的比较分析。为了确定协作网络和知识网络中的知识流,本研究部署了中国国家知识产权局在2000年至2015年期间获取的纳米光电子专利数据库,并从网络角度分析了这两个网络的演变。类型,节点中心性和核心外围结构。结果表明,协作网络和知识网络在负向解耦和发展。具体而言,纳米光电子专利的协作网络规模减小,阻碍了信息的传输,而知识网络规模增大,这有利于知识流。另外,协作网络和知识网络中大多数节点的程度中心和中间中心是正相关的。大学和超微技术在协作网络和知识网络的演进中保持核心地位,并对知识流产生重大影响。这项研究基于对知识网络和协作网络的联合调查,提供了一种可行的知识流识别方法,并阐明了这两个网络中知识管理的效率提升。

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