Publication Type
Journal Article
Version
acceptedVersion
Publication Date
5-2023
Abstract
Existing dynamic weighted graph visualization approaches rely on users’ mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for dynamic weighted graph visualization by explicitly visualizing the differences of graph structures (e.g., edge weight differences) between adjacent timeslices. Specifically, we present a novel nested matrix design that overviews the graph structure differences over a time period as well as shows graph structure details in the timeslices of user interest. By collectively considering the overall temporal evolution and structure details in each timeslice, an optimization-based node reordering strategy is developed to group nodes with similar evolution patterns and highlight interesting graph structure details in each timeslice. We conducted two case studies on real-world graph datasets and in-depth interviews with 12 target users to evaluate DiffSeer. The results demonstrate its effectiveness in visualizing dynamic weighted graphs.
Keywords
Dynamic graph visualization, weighted graph visualization, difference
Discipline
Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization
Publication
IEEE Computer Graphics and Applications
Volume
43
Issue
3
ISSN
0272-1716
Identifier
10.1109/MCG.2023.3248289
Publisher
Institute of Electrical and Electronics Engineers
Citation
WEN, Xiaolin; WANG, Yong; WU, Meixuan; WANG, Fengjie; YUE, Xuanwu; SHEN, Qiaomu; MA, Yuxin; and ZHU, Min.
DiffSeer: Difference-based dynamic weighted graph visualization. (2023). IEEE Computer Graphics and Applications. 43, (3),.
Available at: https://ink.library.smu.edu.sg/sis_research/8600
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/MCG.2023.3248289