LSQT: Low-Stretch Quasi-Trees for Bundling and Layout

TL;DR

LSQT introduces low-stretch quasi-trees for graph visualization, enabling fast, high-fidelity edge bundling and layout that support interactive exploration of large graphs with minimal clutter.

Contribution

The paper presents LSQT, a novel method using low-stretch quasi-trees for efficient, high-quality graph layout and edge bundling without relying on precomputed vertex positions.

Findings

Handles graphs with over 100,000 edges in eight seconds

Provides superior topological fidelity compared to traditional spanning trees

Supports dynamic layout adjustment and interactive querying

Abstract

We introduce low-stretch trees to the visualization community with LSQT, our novel technique that uses quasi-trees for both layout and edge bundling. Our method offers strong computational speed and complexity guarantees by leveraging the convenient properties of low-stretch trees, which accurately reflect the topological structure of arbitrary graphs with superior fidelity compared to arbitrary spanning trees. Low-stretch quasi-trees also have provable sparseness guarantees, providing algorithmic support for aggressive de-cluttering of hairball graphs. LSQT does not rely on previously computed vertex positions and computes bundles based on topological structure before any geometric layout occurs. Edge bundles are computed efficiently and stored in an explicit data structure that supports sophisticated visual encoding and interaction techniques, including dynamic layout adjustment and interactive bundle querying. Our unoptimized implementation handles graphs of over 100,000 edges in eight seconds, providing substantially higher performance than previous approaches.