, Volume 114, Issue 3, pp 1327–1343 | Cite as

Identifying important scholars via directed scientific collaboration networks

  • Jianlin Zhou
  • An Zeng
  • Ying Fan
  • Zengru Di


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.


Scientific collaboration network Credit allocation Centrality analysis 



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).


  1. 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.CrossRefGoogle Scholar
  2. 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.MathSciNetCrossRefzbMATHGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. Colizza, V., Flammini, A., Serrano, M. A., & Vespignani, A. (2006). Detecting rich-club ordering in complex networks. Nature Physics, 2(2), 110–115.CrossRefGoogle Scholar
  5. Ding, Y. (2011). Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. Journal of Informetrics, 5(1), 187–203.CrossRefGoogle Scholar
  6. Ebadi, A., & Schiffauerova, A. (2015). How to become an important player in scientific collaboration networks? Journal of Informetrics, 9(4), 809–825.CrossRefGoogle Scholar
  7. Evans, T. S., Lambiotte, R., & Panzarasa, P. (2011). Community structure and patterns of scientific collaboration in business and management. Scientometrics, 89(1), 381–396.CrossRefGoogle Scholar
  8. Fagiolo, G. (2007). Clustering in complex directed networks. Physical Review E, 76(2), 026107.MathSciNetCrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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.MathSciNetCrossRefzbMATHGoogle Scholar
  12. Gleich, D. F. (2015). PageRank beyond the Web. SIAM Review, 57(3), 321–363.MathSciNetCrossRefzbMATHGoogle Scholar
  13. Hou, H., Kretschmer, H., & Liu, Z. (2007). The structure of scientific collaboration networks in Scientometrics. Scientometrics, 75(2), 189–202.CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. Kintali, S. (2008). Betweenness centrality: Algorithms and lower bounds. arXiv:0809.1906.
  16. Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 604–632.MathSciNetCrossRefzbMATHGoogle Scholar
  17. Leicht, E. A., & Newman, M. E. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. Liu, P., & Xia, H. (2015). Structure and evolution of co-authorship network in an interdisciplinary research field. Scientometrics, 103(1), 101–134.MathSciNetCrossRefGoogle Scholar
  21. 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.MathSciNetCrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. Newman, M. E. (2001). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64(1), 016131.CrossRefGoogle Scholar
  24. 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.MathSciNetCrossRefzbMATHGoogle Scholar
  25. Newman, M. E. (2002). Assortative mixing in networks. Physical Review Letters, 89(20), 208701.CrossRefGoogle Scholar
  26. Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.CrossRefGoogle Scholar
  27. Opsahl, T., Colizza, V., Panzarasa, P., & Ramasco, J. J. (2008). Prominence and control: The weighted rich-club effect. Physical Review Letters, 101(16), 168702.CrossRefGoogle Scholar
  28. 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.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.CrossRefGoogle Scholar
  31. Sinatra, R., Wang, D., Deville, P., Song, C., & Barabsi, A. L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239.CrossRefGoogle Scholar
  32. Sonnenwald, D. H. (2007). Scientific collaboration. Annual Review of Information Science and Technology, 41(1), 643–681.CrossRefGoogle Scholar
  33. 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.CrossRefGoogle Scholar
  34. Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.CrossRefGoogle Scholar
  35. 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
  36. Yan, E., & Ding, Y. (2011). Discovering author impact: A PageRank perspective. Information Processing and Management, 47(1), 125–134.CrossRefGoogle Scholar
  37. 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
  38. 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.CrossRefGoogle Scholar
  39. 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.MathSciNetCrossRefzbMATHGoogle Scholar
  40. 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.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  1. 1.School of Systems ScienceBeijing Normal UniversityBeijingPeople’s Republic of China

Personalised recommendations