Browser-based Hyperbolic Visualization of Graphs

Jacob Miller, Stephen Kobourov, Vahan Huroyan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hyperbolic geometry offers a natural 'focus+context' for data visualization and has been shown to underlie real-world complex networks. However, current hyperbolic network visualization approaches are limited to special types of networks and do not scale to large datasets. With this in mind, we designed, implemented, and analyzed three methods for hyperbolic visualization of networks in the browser based on inverse projections, generalized force-directed algorithms, and hyperbolic multi-dimensional scaling (H-MDS). A comparison with Euclidean MDS shows that H-MDS produces embeddings with lower distortion for several types of networks. All three methods can handle node-link representations and are available in fully functional web-based systems.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 15th Pacific Visualization Symposium, PacificVis 2022
PublisherIEEE Computer Society
Pages71-80
Number of pages10
ISBN (Electronic)9781665423359
DOIs
StatePublished - 2022
Event15th IEEE Pacific Visualization Symposium, PacificVis 2022 - Virtual, Online, Japan
Duration: Apr 11 2022Apr 14 2022

Publication series

NameIEEE Pacific Visualization Symposium
Volume2022-April
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773

Conference

Conference15th IEEE Pacific Visualization Symposium, PacificVis 2022
Country/TerritoryJapan
CityVirtual, Online
Period4/11/224/14/22

Keywords

  • Graph drawing
  • Hyperbolic geometry
  • Non-Euclidean embedding
  • Stochastic gradient descent

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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