Abstract
Interactive visual analysis plays an important role to understand complex dataset. Literature data are most often visualized as collaboration networks to show the connection between researchers. However, the static networks barely transfer much information when the dataset including temporal variable. In this paper, we propose an embedded network visualization to display the temporal patterns hiding in the data and to avoid occlusion by intelligent filters. We research different graph style such as temporal display and direction to find the best way to present the temporal feature. Also, we demonstrate the usability of our approach with case studies on real bibliographic databases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Horak, Z., Kudelka, M., Snasel, V., Abraham, A., Rezankova, H.: Forcoa. NET: an interactive tool for exploring the significance of authorship networks in DBLP data. In: 2011 International Conference on Computational Aspects of Social Networks (CASoN), pp. 261–266. IEEE (2011)
Wang, W., Liu, J., Yu, S., Zhang, C., Xu, Z., Xia, F.: Mining advisor-advisee relationships in scholarly big data: a deep learning approach. In: 2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL), pp. 209–210. IEEE (2016)
Chang, Y.-W., Huang, M.-H., Lin, C.-W.: Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses. Scientometrics 105(3), 2071–2087 (2015)
Ishida, R., Takahashi, S., Wu, H.-Y.: Interactively uncluttering node overlaps for network visualization. In: 2015 19th International Conference on Information Visualisation (iV), pp. 200–205. IEEE (2015)
Isenberg, P., Heimerl, F., Koch, S., Isenberg, T., Xu, P., Stolper, C., Sedlmair, M., Chen, J., Moller, T., Stasko, J.T.: Vispubdata. org: A Metadata Collection about IEEE visualization (VIS) publications. IEEE Trans. Vis. Comput. Graph. 23, 2199–2206 (2016)
Fulda, J., Brehmel, M., Munzner, T.: TimeLineCurator: interactive authoring of visual timelines from unstructured text. IEEE Trans. Vis. Comput. Graph. 22(1), 300–309 (2016)
Nakazawa, R., Itoh, R., Saito, T.: A visualization of research papers based on the topics and citation network. In: 2015 19th International Conference on Information Visualisation (iV), pp. 283–289. IEEE (2015)
Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Springer, London (2011). https://doi.org/10.1007/978-0-85729-079-3
Bach, B., Shi, C., Heulot, N., Madhyastha, T., Grabowski, T., Dragicevic, P.: Time curves: folding time to visualize patterns of temporal evolution in data. IEEE Trans. Vis. Comput. Graph. 22(1), 559–568 (2016)
Bach, B., Dragicevic, P., Archambault, D., Hurter, C., Carpendale, S.: A review of temporal data visualizations based on space-time cube operations. In: Proceedings of Eurographics Conference on Visualization (2014)
Xu, X., Wang, W., Liu, Y., Zhao, X., Xu, Z., Zhou, H.: A bibliographic analysis and collaboration patterns of ieee transactions on intelligent transportation systems between 2000 and 2015. IEEE Trans. Intell. Transp. Syst. 17(8), 2238–2247 (2016)
Xu, X., Jia, W., Tang, M., Feng, Q., Li, Y.: Author cooperation relationship in digital publishing based on social network analysis. In: 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1631–1635. IEEE (2015)
Zhang, J., Chen, C., Li, J.: Visualizing the intellectual structure with paper-reference matrices. IEEE Trans. Vis. Comput. Graph. 15(6), 1153–1160 (2009)
Daud, A., Ahmad, M., Malik, M.S.I., Che, D.: Using machine learning techniques for rising star prediction in co-author network. Scientometrics 102(2), 1687–1711 (2015)
Billah, S.M., Gauch, S.: Social network analysis for predicting emerging researchers. In: 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3 K), vol. 1, pp. 27–35. IEEE (2015)
Soós, S.: Age-sensitive bibliographic coupling reflecting the history of science: the case of the species problem. Scientometrics 98(1), 23–51 (2014)
Daud, A.: Group level temporal academic social network mining using topic models. Tsinghua University (2010)
Jiang, X., Zhang, J.: A text visualization method for cross-domain research topic mining. J. Vis. 19(3), 561–576 (2016)
Varlamis, I., Tsatsaronis, G.: Visualizing bibliographic databases as graphs and mining potential research synergies. In: 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 53–60. IEEE (2011)
Hachul, S., Junger, M.: An experimental comparison of fast algorithms for drawing general large graphs. Proc. Graph Draw. 235–240, 2005 (2005)
Noack, A.: Energy models for graph clustering. J. Graph Alg. Appl. 11(2), 453–480 (2007)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E70, 066111 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Jing, M., Li, X., Hu, Y. (2018). Interactive Temporal Visualization of Collaboration Networks. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_70
Download citation
DOI: https://doi.org/10.1007/978-3-319-77383-4_70
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77382-7
Online ISBN: 978-3-319-77383-4
eBook Packages: Computer ScienceComputer Science (R0)