Dynamic Visualization of Citation Networks and Detection of Influential Node Addition
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.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
- 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.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
- 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