Spherical Graph Drawing by Multi-dimensional Scaling

Jacob Miller, Vahan Huroyan, Stephen Kobourov

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

2 Scopus citations


We describe an efficient and scalable spherical graph embedding method. The method uses a generalization of the Euclidean stress function for Multi-Dimensional Scaling adapted to spherical space, where geodesic pairwise distances are employed instead of Euclidean distances. The resulting spherical stress function is optimized by means of stochastic gradient descent. Quantitative and qualitative evaluations demonstrate the scalability and effectiveness of the proposed method. We also show that some graph families can be embedded with lower distortion on the sphere, than in Euclidean and hyperbolic spaces.

Original languageEnglish (US)
Title of host publicationGraph Drawing and Network Visualization - 30th International Symposium, GD 2022, Revised Selected Papers
EditorsPatrizio Angelini, Reinhard von Hanxleden
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783031222023
StatePublished - 2023
Externally publishedYes
Event30th International Symposium on Graph Drawing and Network Visualization, GD 2022 - Tokyo, Japan
Duration: Sep 13 2022Sep 16 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13764 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference30th International Symposium on Graph Drawing and Network Visualization, GD 2022

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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