Selected Publications

Spatio-temporal diffusion patterns found in Ferguson Unrest dataset in August, 2014. Spatial diffusion patterns in three adjacent time windows (c, d, and e) are examined for investigation of the temporal patterns of information diffusion in a short period.
In ACM Transactions on Intelligence Systems and Technology


  • iStoryline: Effective Convergence to Hand-drawn Storylines

    Tan Tang, Sadia Rubab, Jiewen Lai, Weiwei Cui, Lingyun Yu, Yingcai Wu
    Details PDF Video

  • SocialWave: Visual Analysis of Spatio-temporal Diffusion of Information on Social Media

    Guodao Sun, Tan Tang, Tai-Quan Peng, Ronghua Liang, Yingcai Wu
    Details PDF Video

  • UNMAT: Visual Comparison and Exploration of Uncertainty in Large Graph Sampling

    Tan Tang, Sufei Wang, Yunfeng Li, Bohan Li, Yingcai Wu
    Details PDF Video

Recent & Upcoming Talks



Uncertainty comparison of four sampling approaches and five datasets in terms of node degree distribution. (A) The title of our visualization system UNMAT. (B) Control panel for specifying input parameters. (C) Five datasets acquired from Standford Large Network Collection. (D) Four sampling approaches for comparison and exploration. (E) Spreadsheet-style visualization for comparing different sampling approaches on various contexts. (F) Ranking stair-shape charts according to uncertainty information.


Spatio-temporal diffusion of information on social media during outbreak of 2014 Ebola Epidemics with SocialWave. (a) The overall temporal trend of spatial diffusion among all geolocations over time; (b) spatial visualization for displaying diffusion of information at a given time period; (c) hashtag view for selecting hashtag(s) of interest to be investigated; (d) history view for comparing spatial diffusion patterns found at different time.


I am a teaching assistant for the following courses at Zhejiang University:

  • CS101: Information Visualization
  • CS102: Cross-media Visualization