Dynamic Visualization of Citation Networks and Detection of Influential Node Addition

  • Takayasu Fushimi
  • Tetsuji Satoh
  • Noriko Kando
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


In this paper, to effectively visualize the browsing order of scientific articles, we propose a visualization method for citation networks focusing on the directed acyclic graph (DAG) structure. In our method, all article nodes are embedded into polar coordinate plane, where angular and radial coordinates express the citation relations and order relations among articles, respectively. Furthermore, the proposed method is equipped with a dynamic property to update coordinates of all nodes at low cost when a new article node and citation links are added to the citation network. From experimental evaluations using real citation networks, we confirm that our method explicitly reflects citation relations and browsing order compared with existing methods. Furthermore, focusing on changes in visualization results when new nodes and links are added to the citation network, our method can detect influential node and links addition by angular displacement of each node.



This work was supported by JSPS KAKENHI Grant No.16K16154 and by NII’s strategic open-type collaborative research.


  1. 1.
    Alsakran, J., Chen, Y., Luo, D., Zhao, Y., Yang, J., Dou, W., Liu, S.: Real-time visualization of streaming text with a force-based dynamic system. IEEE Comput. Graph. Appl. 32(1), 34–45 (2012). JanGoogle Scholar
  2. 2.
    Chung, F.R.K.: Spectral Graph Theory. American Mathematical Society, Providence (1997)MATHGoogle Scholar
  3. 3.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)ADSCrossRefGoogle Scholar
  4. 4.
    Fushimi, T., Satoh, T.: Constructing and visualizing topic forests for text streams. In: Proceedings of the 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI’17), pp. 10–17 (2017)Google Scholar
  5. 5.
    Fushimi, T., Kubota, Y., Saito, K., Kimura, M., Ohara, K., Motoda, H.: Speeding up bipartite graph visualization method. AI 2011: Advances in Artificial Intelligence: 24th Australasian Joint Conference, Perth, Australia, 5–8 December 2011. Proceedings, pp. 697–706. Springer, Berlin (2011)Google Scholar
  6. 6.
    Kamada, T., Kawai, S.: An algorithm for drawing general undirected graphs. Inf. Process. Lett. 31, 7–15 (1989)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46, 604–632 (1999)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Torgerson, W.: Multidimensional scaling: I theory and method. Psychometrika 17, 401–419 (1952)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Yamada, T., Saito, K., Ueda, N.: Cross-entropy directed embedding of network data. In: Proceedings of the 20th International Conference on Machine Learning (ICML03), pp. 832–839 (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Computer ScienceTokyo University of TechnologyHachioji-city, TokyoJapan
  2. 2.Faculty of Library, Information and Media ScienceUniversity of TsukubaTsukuba-city, IbarakiJapan
  3. 3.Information and Society Research DivisionNational Institute of InformaticsChiyoda-ku, TokyoJapan

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