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Identifying important scholars via directed scientific collaboration networks

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Abstract

Scientific collaboration plays an important role in the knowledge production and scientific development. Researchers have investigated numerous aspects of scientific collaboration by constructing scientific collaboration networks. And we can perform node centrality analysis on the scientific collaboration networks to identify important scholars. In these collaboration networks, two scientists are linked if they have coauthored at least one paper and the way of constructing these networks is based on the assumption that each author’s contribution to an article is the same. However, the authors’ contributions to an article are unequal in reality and we should pay attention to the impact of this unequal credit allocation on the understanding of scientific collaboration. In this paper, we regard the first author as the most important contributor to an article and build a directed scientific collaboration network. Then we identify important scholars by analyzing this directed network. For one thing, we investigate the difference between the undirected and directed scientific collaboration network in network properties and centrality analysis. For another, we apply different centrality indices: betweenness, PageRank, SIR and HITS to the directed scientific collaboration network. As a result, we find that each indicator has a different performance and the PageRank algorithm and SIR show highly positive correlation with in-degree. The HITS algorithm also shows better property which can hep us distinguish potential young scholars and identify important collaborators.

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References

  • Amjad, T., Ding, Y., Xu, J., Zhang, C., Daud, A., Tang, J., et al. (2017). Standing on the shoulders of giants. Journal of Informetrics, 11(1), 307–323.

    Article  Google Scholar 

  • Barabási, A. L., Jeong, H., Nda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical mechanics and its applications, 311(3), 590–614.

    Article  MathSciNet  MATH  Google Scholar 

  • Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1–7), 107–117.

    Article  Google Scholar 

  • Colizza, V., Flammini, A., Serrano, M. A., & Vespignani, A. (2006). Detecting rich-club ordering in complex networks. Nature Physics, 2(2), 110–115.

    Article  Google Scholar 

  • Ding, Y. (2011). Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. Journal of Informetrics, 5(1), 187–203.

    Article  Google Scholar 

  • Ebadi, A., & Schiffauerova, A. (2015). How to become an important player in scientific collaboration networks? Journal of Informetrics, 9(4), 809–825.

    Article  Google Scholar 

  • Evans, T. S., Lambiotte, R., & Panzarasa, P. (2011). Community structure and patterns of scientific collaboration in business and management. Scientometrics, 89(1), 381–396.

    Article  Google Scholar 

  • Fagiolo, G. (2007). Clustering in complex directed networks. Physical Review E, 76(2), 026107.

    Article  MathSciNet  Google Scholar 

  • Fan, Y., Li, M., Chen, J., Gao, L., Di, Z., & Wu, J. (2004). Network of econophysicists: A weighted network to investigate the development of econophysics. International Journal of Modern Physics B, 18(17n19), 2505–2511.

    Article  Google Scholar 

  • Foster, J. G., Foster, D. V., Grassberger, P., & Paczuski, M. (2010). Edge direction and the structure of networks. Proceedings of the National Academy of Sciences of the United States of America, 107(24), 10815–10820.

    Article  Google Scholar 

  • Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7821–7826.

    Article  MathSciNet  MATH  Google Scholar 

  • Gleich, D. F. (2015). PageRank beyond the Web. SIAM Review, 57(3), 321–363.

    Article  MathSciNet  MATH  Google Scholar 

  • Hou, H., Kretschmer, H., & Liu, Z. (2007). The structure of scientific collaboration networks in Scientometrics. Scientometrics, 75(2), 189–202.

    Article  Google Scholar 

  • Kim, J., & Diesner, J. (2015). Coauthorship networks: A directed network approach considering the order and number of coauthors. Journal of the Association for Information Science and Technology, 66(12), 2685–2696.

    Article  Google Scholar 

  • Kintali, S. (2008). Betweenness centrality: Algorithms and lower bounds. arXiv:0809.1906.

  • Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 604–632.

    Article  MathSciNet  MATH  Google Scholar 

  • Leicht, E. A., & Newman, M. E. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.

    Article  Google Scholar 

  • Li, M., Fan, Y., Chen, J., Gao, L., Di, Z., & Wu, J. (2005). Weighted networks of scientific communication: The measurement and topological role of weight. Physica A: Statistical Mechanics and its Applications, 350(2), 643–656.

    Article  Google Scholar 

  • Liu, X., Bollen, J., Nelson, M. L., & Van de Sompel, H. (2005). Co-authorship networks in the digital library research community. Information processing and management, 41(6), 1462–1480.

    Article  Google Scholar 

  • Liu, P., & Xia, H. (2015). Structure and evolution of co-authorship network in an interdisciplinary research field. Scientometrics, 103(1), 101–134.

    Article  MathSciNet  Google Scholar 

  • Lü, L., Chen, D., Ren, X. L., Zhang, Q. M., Zhang, Y. C., & Zhou, T. (2016). Vital nodes identification in complex networks. Physics Reports, 650, 1–63.

    Article  MathSciNet  Google Scholar 

  • Lu, H., & Feng, Y. (2009). A measure of authors centrality in co-authorship networks based on the distribution of collaborative relationships. Scientometrics, 81(2), 499.

    Article  Google Scholar 

  • Newman, M. E. (2001). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64(1), 016131.

    Article  Google Scholar 

  • Newman, M. E. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 404–409.

    Article  MathSciNet  MATH  Google Scholar 

  • Newman, M. E. (2002). Assortative mixing in networks. Physical Review Letters, 89(20), 208701.

    Article  Google Scholar 

  • Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.

    Article  Google Scholar 

  • Opsahl, T., Colizza, V., Panzarasa, P., & Ramasco, J. J. (2008). Prominence and control: The weighted rich-club effect. Physical Review Letters, 101(16), 168702.

    Article  Google Scholar 

  • Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043), 814.

    Article  Google Scholar 

  • Qi, M., Zeng, A., Li, M., Fan, Y., & Di, Z. (2017). Standing on the shoulders of giants: The effect of outstanding scientists on young collaborators’ careers. Scientometrics, 111(3), 1839–1850.

    Article  Google Scholar 

  • Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004). Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America, 101(9), 2658–2663.

    Article  Google Scholar 

  • Sinatra, R., Wang, D., Deville, P., Song, C., & Barabsi, A. L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239.

    Article  Google Scholar 

  • Sonnenwald, D. H. (2007). Scientific collaboration. Annual Review of Information Science and Technology, 41(1), 643–681.

    Article  Google Scholar 

  • Tijssen, R. J. (2004). Is the commercialisation of scientific research affecting the production of public knowledge? Global trends in the output of corporate research articles. Research Policy, 33(5), 709–733.

    Article  Google Scholar 

  • Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.

    Article  Google Scholar 

  • Yan, E., & Ding, Y. (2009). Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the Association for Information Science and Technology, 60(10), 2107–2118.

    Google Scholar 

  • Yan, E., & Ding, Y. (2011). Discovering author impact: A PageRank perspective. Information Processing and Management, 47(1), 125–134.

    Article  Google Scholar 

  • Yan, E., Ding, Y., & Sugimoto, C. R. (2011). P Rank: An indicator measuring prestige in heterogeneous scholarly networks. Journal of the Association for Information Science and Technology, 62(3), 467–477.

    Google Scholar 

  • Yoshikane, F., Nozawa, T., & Tsuji, K. (2006). Comparative analysis of co-authorship networks considering authors’ roles in collaboration: Differences between the theoretical and application areas. Scientometrics, 68(3), 643–655.

    Article  Google Scholar 

  • Zeng, A., Shen, Z., Zhou, J., Wu, J., Fan, Y., Wang, Y., et al. (2017). The science of science: From the perspective of complex systems. Physics Reports, 714–715, 1–73.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhai, L., Li, X., Yan, X., & Fan, W. (2014). Evolutionary analysis of collaboration networks in the field of information systems. Scientometrics, 101(3), 1657–1677.

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61374175, 61573065 and 61603046) and the Natural Science Foundation of Beijing (Grant No. L160008).

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Authors and Affiliations

  1. School of Systems Science, Beijing Normal University, Beijing, 100875, People’s Republic of China

    Jianlin Zhou, An Zeng, Ying Fan & Zengru Di

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  1. Jianlin Zhou
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  2. An Zeng
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  3. Ying Fan
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  4. Zengru Di
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Correspondence to An Zeng or Ying Fan.

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Zhou, J., Zeng, A., Fan, Y. et al. Identifying important scholars via directed scientific collaboration networks. Scientometrics 114, 1327–1343 (2018). https://doi.org/10.1007/s11192-017-2619-0

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