An evaluation method of scientific research team influence based on heterogeneity and node similarity of content and structure


Science research has general rules of development, is like any other social activity. With the improvement of science and technology, scientific problems have become more complex and systematic, individual approach has been replaced by teamwork in scientific research. This paper takes scientific research team cooperative network as research object, analyzes the influence of scientific research teams in the cooperative network from the aspect of node heterogeneity and node similarity of content and structure, and puts forward the influence evaluation method of scientific research team. A scientific research team cooperation network is constructed as the unweighted and undirected graph by the cooperation relationship data of scientific research teams, including co-author, citation, project cooperation and son on. In this network, the scientific research teams are take nodes, and the cooperative relationships between scientific research teams are take as edges. The major factors of scientific research team influence are analyzed, including node heterogeneity and relationship strength between nodes, then a weight and attributed graph is constructed by the research direction of scientific research team and is weighted based on the similarity of nodes’ content and structure by the SimRank model and the Jaccard similarity method. An influence evaluation method was proposed based on the impact of node subjective heterogeneity and node domain heterogeneity, and An influence spread model based on SIR model was given for verifying the proposed influence evaluation method.

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Correspondence to Fan Tongrang.

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Wenbin, Z., Tongrang, F., Zhixian, Y. et al. An evaluation method of scientific research team influence based on heterogeneity and node similarity of content and structure. J Ambient Intell Human Comput 11, 3617–3626 (2020).

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  • Influence evaluation
  • Scientific research team
  • Cooperation network
  • Heterogeneity
  • Node similarity