2015 IEEE Pacific Visualization Symposium (PacificVis) (2015)
April 14, 2015 to April 17, 2015
Limei Che , Baidu, Inc, China
Jie Liang , Key Laboratory of Machine Perception, (Ministry of Education), and School of EECS, Peking University, China
Xiaoru Yuan , Key Laboratory of Machine Perception, (Ministry of Education), and School of EECS, Peking University, China
Jianping Shen , Baidu, Inc, China
Jinquan Xu , Baidu, Inc, China
Yong Li , Baidu, Inc, China
Visualizing dynamic graphs are challenging due to the difficulty to preserving a coherent mental map of the changing graphs. In this paper, we propose a novel layout algorithm which is capable of maintaining the overall structure of a sequence graphs. Through Laplacian constrained distance embedding, our method works online and maintains the aesthetic of individual graphs and the shape similarity between adjacent graphs in the sequence. By preserving the shape of the same graph components across different time steps, our method can effectively help users track and gain insights into the graph changes. Two datasets are tested to demonstrate the effectiveness of our algorithm.
Layout, Heuristic algorithms, Shape, Visualization, Collaboration, Animation, Force
L. Che, Jie Liang, X. Yuan, Jianping Shen, Jinquan Xu and Yong Li, "Laplacian-based dynamic graph visualization," 2015 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Hangzhou, China, 2015, pp. 69-73.