Abstract
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of science and technology, scientific problems have become more complex and systematic, individual approach has been replaced by teamwork in scientific research.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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References
Albert R, Jeong H, Barabasi AL (2000) Error and attack tolerance of complex networks. Nature 2000(406):378–382
Aral S, Walker D (2012) Identifying influential and susceptible members of social networks. Science 10:337–341
Chen DB, Xiao R, Zeng A (2014) Predicting the evolution of spreading on complex networks. Sci Rep 4:6108
Cui D, Liu Y, Wang R et al (2019) Influence of substrate temperature on the structural and optical properties of ZnO films on flexible substrate by RF magnetron sputtering. Acta Microsc 28(1):23–29
Emelyanov GM, Mikhailov DV, Kozlov AP (2017) The TF-IDF measure and analysis of links between words within N-grams in the formation of knowledge units for open tests. Pattern Recognit Image Anal 27:825
Freeman LC (1979) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239
Guojun M, Songyan X, Dianjun H (2017) Improvement of PageRank model and mining algorithm of microblog user influence. Comput Appl Softw 34(5):28–32
Han Z, Wu Y, Tan XS et al (2015) Ranking key nodes in complex networks by considering structural holes. Acta Phys Sin 64(5):429–437
Hu Q, Yanshen Y, Ma P et al (2013) A new approach to identify influential spreaders in complex networks. Acta Phys Sin 62(14):9–19
Lee S (2017) Improving Jaccard index for measuring similarity in collaborative filtering. In: Kim K, Joukov N (eds) Information science and applications 2017. ICISA 2017. Lecture notes in electrical engineering, 424. Springer, Singapore
Morone F, Makse HA (2015) Influence maximization in complex networks through optimal percolation. Nature 527(7579):544
Newman MEJ (2001a) The structure of scientific collaboration networks. Proc Natl Acad Sci 98(2):404–409
Newman MEJ (2001b) Scientific collaboration networks. I. Network construction and fundamental results. Phys Rev E 64(1):016132
Newman MEJ (2001c) Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys Rev E 64(1):016132
Qing G, Guilin J, Yingke L et al (2017) Probabilistic analysis of stochastic SI epidemic model. Stat Decis 7:21–24
Romero DM, Galuba W, Asur S et al (2011) Influence and passivity in social media. In: Machine learning and knowledge discovery in databases. Springer, Heidelberg, pp 18–33
Saito K, Kimura M, Ohara K et al (2010) Selecting information diffusion models over social networks for behavioral analysis. In: Proceedings of the European conference on machine learning and principles and practice of knowledge discovery in databases, Barcelona, pp 180–195
Shu P, Wang W, Tang M et al (2015) Numerical identification of epidemic thresholds for susceptible-infected-recovered model on finite-size networks. Chaos 25(6):063104
Yan XY (2010) Path-finding algorithm of public transport networks based on bipartite graph model. Comput Eng Appl 46(5):246–248
Yi-Run R, Song-Yang L, Jun-De W et al (2017) Node importance measurement based on neighborhood similarity in complex network. Acta Phys Sin 66(3):371–379
Yunpeng X, Songyang L, Yanbing L (2017) An information diffusion dynamic model based on social influence and mean-field theory. Acta Phys Sin 66(03):233–245
Zhao W, Fan T, Nie Y, Feng W, Wen H (2018) Research on attribute dimension partition based on SVM classifying and MapReduce. Wirel Pers Commun 102(4):2759–2774
Zhao W, Yin Z, Fan T, Luo J (2019) Research on influence spread of scientific research team based on scientific factor quantification of big data. Int J Distrib Sens Netw 15(4):1550147719842158
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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). https://doi.org/10.1007/s12652-019-01547-0
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DOI: https://doi.org/10.1007/s12652-019-01547-0