Browser-based Hyperbolic Visualization of Graphs

Jacob Miller; Stephen Kobourov; Vahan Huroyan

doi:10.1109/PacificVis53943.2022.00016

Browser-based Hyperbolic Visualization of Graphs

Jacob Miller, Stephen Kobourov, Vahan Huroyan

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

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 language	English (US)
Title of host publication	Proceedings - 2022 IEEE 15th Pacific Visualization Symposium, PacificVis 2022
Publisher	IEEE Computer Society
Pages	71-80
Number of pages	10
ISBN (Electronic)	9781665423359
DOIs	https://doi.org/10.1109/PacificVis53943.2022.00016
State	Published - 2022
Event	15th IEEE Pacific Visualization Symposium, PacificVis 2022 - Virtual, Online, Japan Duration: Apr 11 2022 → Apr 14 2022

Publication series

Name	IEEE Pacific Visualization Symposium
Volume	2022-April
ISSN (Print)	2165-8765
ISSN (Electronic)	2165-8773

Conference

Conference	15th IEEE Pacific Visualization Symposium, PacificVis 2022
Country/Territory	Japan
City	Virtual, Online
Period	4/11/22 → 4/14/22

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

Access to Document

10.1109/PacificVis53943.2022.00016

Cite this

Miller, J, Kobourov, S & Huroyan, V 2022, Browser-based Hyperbolic Visualization of Graphs. in Proceedings - 2022 IEEE 15th Pacific Visualization Symposium, PacificVis 2022. IEEE Pacific Visualization Symposium, vol. 2022-April, IEEE Computer Society, pp. 71-80, 15th IEEE Pacific Visualization Symposium, PacificVis 2022, Virtual, Online, Japan, 4/11/22. https://doi.org/10.1109/PacificVis53943.2022.00016

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title = "Browser-based Hyperbolic Visualization of Graphs",

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.",

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doi = "10.1109/PacificVis53943.2022.00016",

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AU - Huroyan, Vahan

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N2 - 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.

AB - 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.

KW - Graph drawing

KW - Hyperbolic geometry

KW - Non-Euclidean embedding

KW - Stochastic gradient descent

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Browser-based Hyperbolic Visualization of Graphs

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this