Sticky Links: Encoding Quantitative Data of Graph Edges

Min Lu, Xiangfang Zeng, Joel Lanir, Xiaoqin Sun, Guozheng Li, Daniel Cohen-Or, Hui Huang

Research output: Contribution to journalArticlepeer-review


Visually encoding quantitative information associated with graph links is an important problem in graph visualization. A conventional approach is to vary the thickness of lines to encode the strength of connections in node-link diagrams. In this paper, we present Sticky Links, a novel visual encoding method that draws graph links with stickiness. Taking the metaphor of links with glues, sticky links represent connection strength using spiky shapes, ranging from two broken spikes for weak connections to connected lines for strong connections. We conducted a controlled user study to compare the efficiency and aesthetic appeal of stickiness with conventional thickness encoding. Our results show that stickiness enables more effective and expressive quantitative encoding while maintaining the perception of node connectivity. Participants also found sticky links to be more aesthetic and less visually cluttering than conventional thickness encoding. Overall, our findings suggest that sticky links offer a promising alternative to conventional methods for encoding quantitative information in graphs.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Early online date22 Apr 2024
StateE-pub ahead of print - 22 Apr 2024

Bibliographical note

Publisher Copyright:


  • Clutter
  • Edge Drawing
  • Encoding
  • Graph drawing
  • Graph Visualization
  • Layout
  • Quantitative Encoding
  • Shape
  • Uncertainty
  • Visualization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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