Towards Improved Visualization of Citation Networks
  • Jason WilkinsEmail author
  • Jaakko Järvi
  • Ajit Jain
  • Gaurav Kejriwal
  • Andruid Kerne
  • Vijay Gumudavelly
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9299)


EvolutionWorks supports exploratory browsing of the academic paper citation network with an animated and zoom-able visualization that helps researchers explore the conceptual space that emerges from the relationships between academic papers. Metaphorically speaking, a researcher starts out with the seed of an idea that will grow into an unwieldy set of potentially useful papers that the researcher must prune into a final reading list. Accordingly, EvolutionWorks provides novel affordances to explore the citation network based on this seed-grow-prune model. First, kinetic layering represents abstract document properties as physical properties in a force-directed layout. Second, a unified layout shows the network graph and documents in a single view. Third, the focus-context-focus hop is a way to change focus from paper to paper that keeps researchers aware of the immediate context. Finally, if there is a tight cluster of papers, the system automatically creates cluster summary titles that are easier to read.


Citation networks Graph visualization Information retrieval 


  1. 1.
    Herman, I., Melançon, G., Marshall, M.S.: Graph visualization and navigation in information visualization: a survey. IEEE Trans. Visual Comput. Graphics 6, 24–43 (2000)CrossRefGoogle Scholar
  2. 2.
    Heer, J., Boyd, D.: Vizster: visualizing online social networks. In: Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization, pp. 532–539. IEEE Computer Society, Washington, DC, USA (2005)Google Scholar
  3. 3.
    Von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J.J., Fekete, J.-D., Fellner, D.W.: Visual analysis of large graphs: state-of-the-art and future research challenges. Comput. Graphics Forum 30, 1719–1749 (2011)CrossRefGoogle Scholar
  4. 4.
    Bertin, J.: Semiology of Graphics: Diagrams, Networks, Maps. University of Wisconsin Press, Madison (1983)Google Scholar
  5. 5.
    Kerne, A., Webb, A.M., Smith, S.M., Linder, R., Lupfer, N., Qu, Y., Moeller, J., Damaraju, S.: Using metrics of curation to evaluate information-based ideation. ACM ToCHI 21, 48 (2014)CrossRefGoogle Scholar
  6. 6.
    Jain, A., Lupfer, N., Qu, Y., Linder, R., Kerne, A., Smith, S.M.: Evaluating TweetBubble with ideation metrics of exploratory browsing. In: Proceedings of Creativity and Cognition. ACM (2015)Google Scholar
  7. 7.
    Mostafa, J.: Seeking better web searches. Sci. Am. 292, 66–73 (2005)CrossRefGoogle Scholar
  8. 8.
    Fast, K.V., Campbell, D.G.: “I still like Google”: University student perceptions of searching OPACs and the web. Proc. Am. Soc. Inf. Sci. Technol. 41, 138–146 (2004)CrossRefGoogle Scholar
  9. 9.
    Griffiths, J.R., Brophy, P.: Student searching behavior and the web: use of academic resources and Google. Libr. Trends 53, 539–554 (2005)Google Scholar
  10. 10.
    Bell, S.J.: The Infodiet: how libraries can offer an appetizing alternative to Google. Chronicle Higher Educ. 50, B15 (2004)Google Scholar
  11. 11.
    Rowlands, I., Nicholas, D., Williams, P., Huntington, P., Fieldhouse, M., Gunter, B., Withey, R., Jamali, H.R., Dobrowolski, T., Tenopir, C.: The Google generation: the information behaviour of the researcher of the future. Aslib Proc. 60, 290–310 (2008)CrossRefGoogle Scholar
  12. 12.
    Bates, M.J.: The design of browsing and Berrypicking techniques for the online search interface. Online Rev. 13, 407–424 (1989)CrossRefGoogle Scholar
  13. 13.
    Koh, E., Dworaczyk, B., Albea, J., Hill, R., Choi, H., Caruso, D., Graeber, R., Mistrot, J.M., Smith, S.M., Webb, A., Kerne, A.: combinFormation: a mixed-initiative system for representing collections as compositions of image and text surrogates. In: Joint Conference on Digital Libraries, vol. 0, pp. 11–20 (2006)Google Scholar
  14. 14.
    Eades, P.: A heuristics for graph drawing. Congressus Numerantium 42, 146–160 (1984)MathSciNetGoogle Scholar
  15. 15.
    Fruchterman, T.M., Reingold, E.M.: Graph drawing by force-directed placement. Softw. Pract. Experience 21, 1129–1164 (1991)CrossRefGoogle Scholar
  16. 16.
    Kamada, T., Kawai, S.: An algorithm for drawing general undirected graphs. Inf. Process. Lett. 31, 7–15 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Frick, A., Ludwig, A., Mehldau, H.: A fast adaptive layout algorithm for undirected graphs (extended abstract and system demonstration). In: Tamassia, R., Tollis, I.G. (eds.) GD 1994. LNCS, vol. 894, pp. 388–403. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  18. 18.
    Tufte, E.R.: Envisioning information. Optometry Vision Sci. 68, 322–324 (1991)CrossRefGoogle Scholar
  19. 19.
    Lum, E.B., Stompel, A., Ma, K.-L.: Kinetic visualization: a technique for illustrating 3D shape and structure. In: Visualization, pp. 435–442 (2002)Google Scholar
  20. 20.
    Wallach, H., O’Connell, D.N.: The kinetic depth effect. J. Exp. Psychol. 45, 205–217 (1953)CrossRefGoogle Scholar
  21. 21.
    Johansson, G.: Visual perception of biological motion and a model for its analysis. Percept. Psychophys. 14, 201–211 (1973)CrossRefGoogle Scholar
  22. 22.
    Simons, D.J., Levin, D.T.: Change blindness. Trends Cogn. Sci. 1, 261–267 (1997)CrossRefGoogle Scholar
  23. 23.
    Elmqvist, N., Tsigas, P.: CiteWiz: a tool for the visualization of scientific citation networks. Inf. Visual. 6, 215–232 (2007)CrossRefGoogle Scholar
  24. 24.
    Elmqvist, N., Tsigas, P.: Causality visualization using animated growing polygons. In: IEEE Symposium on Information Visualization, pp. 189–196 (2003)Google Scholar
  25. 25.
    Bergstrom, P., Atkinson, D.C.: Augmenting the exploration of digital libraries with web-based visualizations. In: Fourth International Conference on Digital Information Management, pp. 1–7 (2009)Google Scholar
  26. 26.
    Keim, D.A., Schneidewind, J.O., Sips, M.: CircleView: a new approach for visualizing time-related multidimensional data sets. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 179–182. ACM, Gallipoli, Italy (2004)Google Scholar
  27. 27.
    Small, H.: Co-citation in the scientific literature: a new measure of the relationship between two documents. J. Am. Soc. Inf. Sci. 24, 265–269 (1973)CrossRefGoogle Scholar
  28. 28.
    Chen, T.T., Hsieh, L.C.: On visualization of cocitation networks. In: Proceedings of the 11th International Conference on Information Visualization pp. 470–475. IEEE Computer Society (2007)Google Scholar
  29. 29.
    Chen, T.T., Yen, D.C.: CociteSeer: a system to visualize large cocitation networks. Electron. Libr. 28, 477–491 (2010)CrossRefGoogle Scholar
  30. 30.
    Blythe, J., Patwardhan, M., Oates, T., desJardins, M., Rheingans, P.: Visualization support for fusing relational, spatio-temporal data: building career histories. In: Proceedings of the 9th International Conference on Information Fusion, pp. 1–7 (2006)Google Scholar
  31. 31.
    Morris, S.A., Yen, G., Wu, Z., Asnake, B.: Time line visualization of research fronts. J. Am. Soc. Inform. Sci. Technol. 54, 413–422 (2003)CrossRefGoogle Scholar
  32. 32.
    Chou, J.-K., Yang, C.-K.: PaperVis: literature review made easy. Comput. Graphics Forum 30, 721–730 (2011)CrossRefGoogle Scholar
  33. 33.
    Risden, K., Czerwinski, M.P., Munzner, T., Cook, D.B.: An initial examination of ease of use for 2D and 3D information visualizations of web content. Int. J. Hum. Comput. Stud. 53, 695–714 (2000)CrossRefzbMATHGoogle Scholar
  34. 34.
    Perlin, K., Fox, D.: Pad: an alternative approach to the computer interface. In: Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques, pp. 57–64. ACM, Anaheim, CA (1993)Google Scholar
  35. 35.
    Van Rijsbergen, C.J., Robertson, S.E., Porter, M.F.: New models in probabilistic information retrieval. Computer Laboratory, University of Cambridge (1980)Google Scholar
  36. 36.
    Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. In: Peter, W. (ed.) Document Retrieval Systems, pp. 132–142. Taylor Graham Publishing, London (1988)Google Scholar
  37. 37.
    Bastian, M., Heymann, S., Jacomy, M.: Gephi: An open source software for exploring and manipulating networks. In: Third International AAAI Conference on Weblogs and Social Media, pp. 361–362. AAAI Publications (2009)Google Scholar
  38. 38.
    Gephi. Accessed 12 May 2015
  39. 39.
    OpenGL. Accessed 12 May 2015
  40. 40.
    TWL—Themable Widget Library. Accessed 12 May 2015
  41. 41.
    Kerne, A., Qu, Y., Webb, A.M., Damaraju, S., Lupfer, N., Mathur, A.: Meta-metadata: a metadata semantics language for collection representation applications. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1129–1138. ACM, Toronto, ON, Canada (2010)Google Scholar
  42. 42.
    Qu, Y., Kerne, A., Lupfer, N., Linder, R., Jain, A.: Metadata type system: integrate presentation, data models and extraction to enable exploratory browsing interfaces. In: Proceedings of EICS. ACM (2014)Google Scholar
  43. 43.
    Google Scholar. Accessed 12 May 2015
  44. 44.
    ACM Digital Library. Accessed 12 May 2015
  45. 45.
    IEEE Xplore. Accessed 12 May 2015
  46. 46.
    CiteSeer. Accessed 12 May 2015
  47. 47.
    Redner, S.: How popular is your paper? An empirical study of the citation distribution. Eur. Phys. J. B Condens. Matter Complex Syst. 4, 131–134 (1998)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Jason Wilkins
    • 1
    Email author
  • Jaakko Järvi
    • 1
  • Ajit Jain
    • 1
  • Gaurav Kejriwal
    • 1
  • Andruid Kerne
    • 1
  • Vijay Gumudavelly
    • 1
  1. 1.Texas A&M UniversityCollege StationUSA

Personalised recommendations