Analysis of Knowledge Flow Based on Academic Social Networks: A Case Study of ScienceNet.cn
Xiaolan Wu1,2,Chengzhi Zhang2()
1School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233030, China 2School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
[Objective] This study aims to explore the knowledge flow on academic social networks. [Methods] Take ScienceNet.cn as the representative, we first collect all the data about users’ research directions and friends. Then, we use the simple correlation coefficient to measure the distribution relation of knowledge flow of different disciplines users, and adopt Louvain algorithm to detect the community structure among first-level disciplines. [Results] It is found that the knowledge flow of different disciplines is similar to each other through simple correlation coefficient. There are four knowledge-flow communities among first-level disciplines detected by Louvain algorithm. [Limitations] We construct knowledge flow network only based on friends’ relationship, without considering comments and recommendation relationship. [Conclusions] Through our research, we find that “Life Science” and “Medical Science” showed the most obvious disciplinary affinity in ScienceNet.cn. In addition, there are four main knowledge flow paths cross discipline departments, such as “Earth Science - Life Science - Medical Science”, “Chemical Science - Engineering Material - Mathematical Science-Information Science”, “Earth Science - Engineering Materials”, “Information Science - Management Science”.
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Xiaolan Wu,Chengzhi Zhang. Analysis of Knowledge Flow Based on Academic Social Networks: A Case Study of ScienceNet.cn. Data Analysis and Knowledge Discovery, 2019, 3(4): 107-116.
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