Design Considerations for Optimizing Storyline Visualizations

By Yuzuru Tanahashi, Kwan-Liu Ma. 2012

213 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Shixia Liu
2. Mengchen Liu
3. Yingcai Wu
4. Jun Zhu
5. Weiwei Cui
6. Chongxuan Li
7. Huamin Qu
8. Ronghua Liang

In Sociology and Political Science

1. Shixia Liu
2. Yingcai Wu
3. Mengchen Liu
4. Weiwei Cui
5. Enxun Wei
6. Hao Wei
7. Chien-Hsin Hsueh
8. Ronghua Liang

Citations in the corpus

• Towards Better Analysis of Deep Convolutional Neural Networks (2016, background): “m of a deep CNN. In the field of visualization, researchers have achieved a great deal of success in modeling domain-specific data as a DAG. Typical data includes dynamic relationships between entities [34, 50, 51], temporal topic data [10, 14, 47, 56], temporal event sequences [54], evolving egocentr”
• A survey on information visualization: recent advances and challenges (2014, methodology): “As a result, recent methods focus more on showing dynamic graphs statically [20,91,133].”
• An Efficient Framework for Generating Storyline Visualizations from Streaming Data (2015, methodology): “Index Terms—Storyline visualization, streaming data, layout algorithms, time-varying data Ç”
• How Hierarchical Topics Evolve in Large Text Corpora (2014, methodology): “To overcome this limitation, Tanahashi and Ma [35] formulate the storyline layout as an optimization problem.”
• A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges (2013, methodology): “Tanahashi and Ma[109] described a set of design principles, such as the principles for reducing line crossings and wiggles, for creating proper storyline layouts, and used a genetic optimization algorithm to automate the layout generation process.”
• Visual Analysis of Topic Competition on Social Media (2013, methodology): “Yuzuru and Ma [39] refined the design criteria and used a genetic algorithm to generate an aesthetically-appealing and legible storyline layout.”
• StoryFlow: Tracking the Evolution of Stories (2013, methodology): “A natural solution is to use a genetic algorithm as in [42], to find a nearly optimal layout.”