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Information visualization (InfoVis) uses visual elements to represent abstract data. It communicates information with people by making use of human vision, which is recognized as having the widest bandwidth of all senses. The goal of InfoVis is to seamlessly integrate the visual representations and explorative interfaces together, aiming to provide users with an informative, convenient, and pleasant data exploring and communicating environment.
Our research in information visualization focuses on structured data visualization and query-driven interaction. Graphs and trees are the most classic types of structures. We study the visual representation and the layout algorithms of these structures to fulfill the desired visual properties. As interactive query becomes an indispensable means for data analysis, we study the visual operations that can assist the user to glean insight into the data.
Publications:
Publications:
- Junpeng Wang, Liang Gou , Hao Yang, and Han-Wei Shen: GANViz: A Visual Analytics Approach to Understand the Adversarial Game, IEEE Trans. Vis. Comput. Graph. [IEEE PacificVis 2018 Best Paper Award]
- Junpeng Wang, Xiaotong Liu, Han-Wei Shen: High-dimensional data analysis with subspace comparison using matrix visualization, Information Visualization 2017
- Xiaotong Liu, Han-Wei Shen: Association Analysis for Visual Exploration of Multivariate Scientific Data Sets. IEEE Trans. Vis. Comput. Graph. 22(1): 955-964 (2016)
- Xiaotong Liu, Anbang Xu, Liang Gou, Haibin Liu, Rama Akkiraju, Han-Wei Shen: SocialBrands: Visual analysis of public perceptions of brands on social media. VAST 2016: 71-80
- Xiaotong Liu, Han-Wei Shen, Yifan Hu: Supporting multifaceted viewing of word clouds with focus+context display. Information Visualization 14(2): 168-180 (2015)
- Xiaotong Liu, Han-Wei Shen: The Effects of Representation and Juxtaposition on Graphical Perception of Matrix Visualization. CHI 2015: 269-278
- Xiaotong Liu, Srinivasan Parthasarathy, Han-Wei Shen, Yifan Hu: GalaxyExplorer: Influence-Driven Visual Exploration of Context-Specific Social Media Interactions. WWW (Companion Volume) 2015: 215-218
- Sheng-Jie Luo, Li-Ting Huang, Bing-Yu Chen, Han-Wei Shen: EmailMap: Visualizing Event Evolution and Contact Interaction within Email Archives. PacificVis 2014: 320-324
- Ying Tu, Han-Wei Shen: GraphCharter: Combining browsing with query to explore large semantic graphs. PacificVis 2013: 49-56
- Xiaotong Liu, Yifan Hu, Stephen C. North, Han-Wei Shen: CompactMap: A mental map preserving visual interface for streaming text data. BigData Conference 2013: 48-55
- Kun-Chuan Feng, Chaoli Wang, Han-Wei Shen, Tong-Yee Lee: Coherent Time-Varying Graph Drawing with Multifocus+Context Interaction. IEEE Trans. Vis. Comput. Graph. 18(8): 1330-1342 (2012)
- Abon Chaudhuri, Han-Wei Shen: A self-adaptive treemap-based technique for visualizing hierarchical data in 3D. PacificVis 2009: 105-112
- Boonthanome Nouanesengsy, Sang-Cheol Seok, Han-Wei Shen, Veronica J. Vieland: Using projection and 2D plots to visually reveal genetic mechanisms of complex human disorders. IEEE VAST 2009: 171-178
- Ying Tu, Han-Wei Shen: Balloon Focus: a Seamless Multi-Focus+Context Method for Treemaps. IEEE Trans. Vis. Comput. Graph. 14(6): 1157-1164 (2008)
- Ying Tu, Han-Wei Shen: Visualizing Changes of Hierarchical Data using Treemaps. IEEE Trans. Vis. Comput. Graph. 13(6): 1286-1293 (2007)
- Chaoli Wang, Han-Wei Shen: LOD Map - A Visual Interface for Navigating Multiresolution Volume Visualization. IEEE Trans. Vis. Comput. Graph. 12(5): 1029-1036 (2006)
- Chaoli Wang, Han-Wei Shen: Hierarchical Navigation Interface: Leveraging Multiple Coordinated Views for Level-of-Detail Multiresolution Volume Rendering of Large Scientific Data Sets. IV 2005: 259-267
- Udeepta Bordoloi, David L. Kao, Han-Wei Shen: Visualization techniques for spatial probability density function data. Data Science Journal 3: 153-162 (2004)
- Udeepta Bordoloi, David L. Kao, Han-Wei Shen: Visualization and exploration of spatial probability density functions: a clustering-based approach. Visualization and Data Analysis 2004: 57-64
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