VISPubComPAS: a comparative analytical system for visualization publication data

  • Yang Wang
  • Minzhu Yu
  • Guihua ShanEmail author
  • Han-Wei Shen
  • Zhonghua Lu
Regular Paper


For an unfamiliar field, researchers who are looking for interdisciplinary collaboration or students who are going to start their research career often need to look for top research affiliations and domain experts according to the publication of top conferences or journals in this field. Further comparative analysis of affiliations or experts with similar achievements is also needed in order to find suitable collaborators or supervisors. In this work, we provide comprehensive visual analysis of research affiliations and domain experts based on papers accepted by the IEEE VIS from 1990 to 2018. First, we extract multi-word keywords from title and abstract automatically and then extract topics using LDA model based on these keywords. Second, we extract relationship between authors and affiliations based on co-author analysis. Third, we design and implement VISPubComPAS, a requirement-driven analysis system to (1) help users discover top affiliations and experts of required keywords; (2) visualize the relationships and statistics of these affiliations and experts; (3) compare two selected affiliations or experts of interest in detail by visualization. Finally, we conduct use cases and user reviews to demonstrate the effectiveness of VISPubComPAS.

Graphic abstract


Comparison Scientific literature Topic extraction Visual analytics system Publication analysis Author analysis 



This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDA19080102, the 13th Five-year Informatization Plan of Chinese Academy of Sciences, Grant No. XXH13504 and the Key Research Program of Frontier Sciences, CAS, Grant No. QYZDB-SSW-SMC004-02.

Supplementary material

Supplementary material 1 (MP4 9909 kb)


  1. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993–1022zbMATHGoogle Scholar
  2. Chuang J, Manning CD, Heer J (2012) Termite: visualization techniques for assessing textual topic models. In: Proceedings of the international working conference on advanced visual interfaces. ACMGoogle Scholar
  3. Chuang J et al. (2013) Topic model diagnostics: assessing domain relevance via topical alignment. In: International conference on machine learningGoogle Scholar
  4. Görg C, Liu Z, Kihm J et al (2013) Combining computational analyses and interactive visualization for document exploration and sensemaking in jigsaw. IEEE Trans Visual Comput Graph 19(10):1646–1663CrossRefGoogle Scholar
  5. Guo H, Laidlaw DH (2018) Topic-based exploration and embedded visualizations for research idea generation. In: IEEE transactions on visualization and computer graphicsGoogle Scholar
  6. Isenberg P et al. (2015) Visualization publication dataset. Published June 2015
  7. Isenberg P, Isenberg T, Sedlmair M et al (2017a) Visualization as seen through its research paper keywords[J]. IEEE Trans Visual Comput Graph 23(1):771–780CrossRefGoogle Scholar
  8. Isenberg P, Heimerl F, Koch S et al (2017b) vispubdata. org: a metadata collection about ieee visualization (vis) publications. IEEE Trans Vis Comput Graph 23(9):2199–2206CrossRefGoogle Scholar
  9. Latif S, Beck F (2018) VIS author profiles: interactive descriptions of publication records combining text and visualization. In: IEEE transactions on visualization and computer graphics, pp. 1–1Google Scholar
  10. Liu S et al. (2018) Bridging text visualization and mining: a task-driven survey. In: IEEE Trans Vis Comput GraphGoogle Scholar
  11. Maguire E, Montull JM, Louppe G (2016) Visualization of publication impact. arXiv preprint: arXiv:1605.06242
  12. Matejka J, Grossman T, Fitzmaurice G (2012) Citeology: visualizing paper genealogy. In: CHI’12 extended abstracts on human factors in computing systems. ACM, pp. 181–190Google Scholar
  13. Sievert C, Shirley K (2014) LDAvis: a method for visualizing and interpreting topics. In: Proceedings of the workshop on interactive language learning, visualization, and interfaces, pp. 63–70Google Scholar
  14. Sinha A, Shen Z, Song Y, Ma H, Eide D, Hsu BJ, Wang K (2015) An overview of microsoft academic service (MA) and applications. In: Proceedings of the 24th international conference on world wide web (WWW ‘15 Companion). ACM, New York, NY, USA, pp. 243–246Google Scholar
  15. Stasko J, Choo J, Han Y et al. (2013) Citevis: exploring conference paper citation data visually. In: Posters of IEEE InfoVis, 2Google Scholar
  16. van Raan AFJ (2004) Sleeping beauties in science. Scientometrics 59(3):467–472CrossRefGoogle Scholar
  17. Wang Y, Shi C, Li L, Tong H, Qu H (2018) Visualizing research impact through citation data. ACM Trans Interact Intell Syst (TiiS) 8(1):5Google Scholar
  18. Wu Y, Pitipornvivat N, Zhao J, Yang S, Huang G, Qu H (2015) egoSlider: visual analysis of egocentric network evolution. IEEE Trans Vis Comput Graph 22(1):260–269CrossRefGoogle Scholar

Copyright information

© The Visualization Society of Japan 2019

Authors and Affiliations

  • Yang Wang
    • 1
  • Minzhu Yu
    • 1
  • Guihua Shan
    • 1
    Email author
  • Han-Wei Shen
    • 2
  • Zhonghua Lu
    • 1
  1. 1.Computer Network Information CenterChinese Academy of SciencesBeijingChina
  2. 2.The Ohio State UniversityColumbusUSA

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