Method for Judging Interdisciplinary References in Literature Based on Complex Networks

  • Xiwen Zhang
  • Shuo Xu
  • Xin AnEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1146)


The arrival of information age provides a platform for the development of academic field. Knowledge exchanges between disciplines are becoming more frequent, boundaries are gradually blurred, interdisciplinary has become an inevitable trend. In the field of Scientometrics, it has become a new hotspot to study the interdisciplinarity of specific documents by identifying the subject characteristics of references. However, facing a massive academic documents, how to identify the subject characteristics of references quickly and accurately still restricts the development of related research. In this paper, we use the reference data in the field of Gene Editing of 2015 from Web of Science core database, with the help of the indicators of complex network link prediction, five features are selected. Base on this, a non-linear Support Vector Machine classification model is constructed to judge the interdisciplinarity of citation. Finally, the F1 value obtained by 5 fold cross-validation is 0.63, which indicators that the indicators can distinguish the academic citation from the interdisciplinary citation.


Citation analysis Interdisciplinary SVM 


  1. 1.
    Tan, Y.T., Deng, X.Z.: Research review on interdisciplinary information seeking behaviors. Libr. Inf. Serv. 59(16), 135–142 (2015)Google Scholar
  2. 2.
    Han, P., Wang, D.B.: A Review on theories and practices of interdisciplinarity. J. China Soc. Sci. Tech. Inf. 11, 1222–1232 (2014)Google Scholar
  3. 3.
    Larivière, V., Gingras, Y.: On the relationship between interdisciplinarity and scientific impact. J. Am. Soc. Inf. Sci. Technol. 61(1), 126–131 (2010)CrossRefGoogle Scholar
  4. 4.
    Silva, F.N., Rodrigues, F.A., Oliveira, O.N., et al.: Quantifying the interdisciplinarity of scientific journals and fields. J. Informetr. 7(2), 469–477 (2013)CrossRefGoogle Scholar
  5. 5.
    Fiala, F.: PageRank-based prediction of award-winning researchers and the impact of citations. J. Inf. 11, 1044–1068 (2017)Google Scholar
  6. 6.
    Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B-Condens. Matter Complex Syst. 71(4), 623–630 (2009)CrossRefGoogle Scholar
  7. 7.
    Shibata, N., Kajikawa, Y., Sakata, I.: Link prediction in citation networks. J. Assoc. Inf. Sci. Technol. 63(1), 78–85 (2011)CrossRefGoogle Scholar
  8. 8.
    Chakraborty, T., Kumar, S., Goyal, P., et al.: On the categorization of scientific citation profiles in computer science. Commun. ACM 58(9), 82–90 (2015)CrossRefGoogle Scholar
  9. 9.
    Small, H.: Maps of science as interdisciplinary discourse: co-citation contexts and the role of analogy. Scientometrics 83(3), 835–849 (2010)CrossRefGoogle Scholar
  10. 10.
    Chakraborty, T., Kumar, S., Reddy, M.D., et al.: Automatic classification and analysis of interdisciplinary fields in computer sciences. In: International Conference on Social Computing, pp. 180–187. IEEE (2013)Google Scholar
  11. 11.
    Hasan, M.A., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: The Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism and Security, Bethesda (2006)Google Scholar
  12. 12.
    Chen, W.J., Zhang, D.B.: Study on the internal factors and effects of gene editing technology development. Genomics Appl. Biol. 1–8, 07 January 2020.
  13. 13.
    Liu, Q., Li, Y., Duan, H., et al.: Knowledge graph construction techniques. J. Comput. Res. Dev. 53(03), 582–600 (2016)Google Scholar
  14. 14.
    Hsu, C., Chang, C., Lin, C.: A practical guide to support vector classification. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan (2003).

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Economics and ManagementBeijing Forestry UniversityBeijingChina
  2. 2.Research Base of Modern Manufacturing Development, College of Economics and ManagementBeijing University of TechnologyBeijingChina

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