Issue No. 12 - Dec. (2016 vol. 22)
Xiting Wang , Tsinghua University, Beijing
Shixia Liu , Tsinghua University, Beijing
Junlin Liu , Tsinghua University, Beijing
Jianfei Chen , Tsinghua University, Beijing
Jun Zhu , Tsinghua University, Beijing
Baining Guo , Microsoft Research, Beijing, China
This paper presents a visual analytics approach to analyzing a full picture of relevant topics discussed in multiple sources, such as news, blogs, or micro-blogs. The full picture consists of a number of common topics covered by multiple sources, as well as distinctive topics from each source. Our approach models each textual corpus as a topic graph. These graphs are then matched using a consistent graph matching method. Next, we develop a level-of-detail (LOD) visualization that balances both readability and stability. Accordingly, the resulting visualization enhances the ability of users to understand and analyze the matched graph from multiple perspectives. By incorporating metric learning and feature selection into the graph matching algorithm, we allow users to interactively modify the graph matching result based on their information needs. We have applied our approach to various types of data, including news articles, tweets, and blog data. Quantitative evaluation and real-world case studies demonstrate the promise of our approach, especially in support of examining a topic-graph-based full picture at different levels of detail.
Visualization, Measurement, Algorithm design and analysis, Data models, Games, Google, Smart phones
X. Wang, S. Liu, J. Liu, J. Chen, J. Zhu and B. Guo, "TopicPanorama: A Full Picture of Relevant Topics," in IEEE Transactions on Visualization & Computer Graphics, vol. 22, no. 12, pp. 2508-2521, 2016.