Browser-based Hyperbolic Visualization of Graphs

TL;DR

This paper introduces three scalable browser-based hyperbolic visualization methods for complex networks, outperforming Euclidean approaches in embedding accuracy and supporting large datasets.

Contribution

It presents novel hyperbolic visualization techniques implemented in the browser, enabling scalable and accurate network visualization for diverse network types.

Findings

H-MDS yields lower distortion embeddings than Euclidean MDS.

All methods support node-link representations in web systems.

The approaches handle large datasets effectively.

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.