报告时间：2016年3月12日（星期六），上午10：30；
报告地点：浙江大学紫金港校区蒙民伟楼CAD&CG国家重点实验室402室
报告题目：Edge Bundling in Information Visualization
报告人：Prof.Kang Zhang
主持人：陈为 教授
Abstract:Edge bundling has been widely used to reduce visual clutter and reveal high-level edge patterns for large graphs in information visualization. Due to strong edge attraction, bundled results often show considerable curvature and tangling at bundle intersections. Inappropriate bundling may fail to reveal true data patterns and even mislead users. This talk presents a parametrizable 6-step edge bundling framework called LEB that clearly reveals the patterns of the input graph, with distinguishable and traceable bundles. The bundling results by LEB are also easily adjustable by tuning a small number of parameters. We have conducted a user experiment to test and compare LEB with previous approaches. The experiment on three datasets (including two common ones) demonstrates LEB’s superiority over previous approaches in visualizing data patterns. Our implementation with reusable computation also delivers an execution speed fast enough for real-time interaction and animation.
Finally, I will mention other on-going research in information visualization and generative art.
Bio:Kang Zhang is Professor and Director of Visual Computing Lab, Department of Computer Science, and an Adjunct Professor in School of Arts and Technology, the University of Texas at Dallas. He received his B.Eng. in Computer Engineering from University of Electronic Science and Technology of China in 1982, and Ph.D. from the University of Brighton, UK, in 1990. Prior to joining UT-Dallas, he held academic positions in the UK, Australia, and China. Dr. Zhang's research interests include information visualization, visual languages, aesthetic computing, and software engineering; and has published over 200 papers in these areas. He has authored and edited six books. Dr Zhang is on the Editorial Boards of Journal of Visual Languages and Computing, Journal of Big Data, The Visual Computer, International Journal of Software Engineering and Knowledge Engineering, and International Journal of Advanced Intelligence. His home page is at www.utdallas.edu/~kzhang.
