Research on influence spread of scientific research team based on scientific factor quantification of big data

Wenbin Zhao,Jishuang Luo,Zhixian Yin,Tongrang Fan

doi:10.1177/1550147719842158

Wenbin Zhao, Jishuang Luo + Show 2 more

Open Access

https://doi.org/10.1177/1550147719842158

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Abstract

With the development of science and technology, the interactions among scientific research teams become more and more frequent, and their relationship and behavior become more and more complex. Man...

Highlights

With the development of science and technology, scientific research cooperation becomes frequent among any subject such as person or team, so that their behavior and relationship become more and more complex
According to the above experiments, the relationship between influence spread and the number of related communities is greater than the number of adjacent nodes
From the two aspects of community and importance, the influence spread of scientific research team is analyzed by complex network, which is constructed for scientific research team relationship based on scientific research factor quantification of ability, resource, activity, and familiarity

Summary

Introduction

With the development of science and technology, scientific research cooperation becomes frequent among any subject such as person or team, so that their behavior and relationship become more and more complex. This indicates that the structure of network is related to the existence of edge between nodes and the weight of edges; so, the proposed network accurately reflects the relationship between scientific research team. Based on scientific research team relationship network, the Kendall rank is calculated for scientific research teams’ influence spread (IS) and the evaluation index of complex network, respectively, such as degree centrality of unweighted network (DC) and degree centrality of weighted network (WDC), closeness centrality (CC), betweenness centrality (BC), and k-core (KC). The calculation of weighted network is better than unweighted network; this shows that the information carried by weighted edge is indispensable for the evaluation of node influence

Conclusion

Declaration of conflicting interests