VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection

TL;DR

VSAL introduces an adaptive visualization framework that dynamically generates informative graph layouts, significantly enhancing the accuracy of vision-based graph property detection over fixed-layout methods.

Contribution

The paper presents VSAL, a novel vision-based approach with an adaptive layout generator, improving graph property detection by tailoring visualizations to individual graph instances.

Findings

VSAL outperforms existing vision-based methods on multiple graph property tasks.

Adaptive layouts lead to better detection accuracy compared to fixed visualizations.

Extensive experiments validate the effectiveness of the proposed framework.

Abstract

Graph property detection aims to determine whether a graph exhibits certain structural properties, such as being Hamiltonian. Recently, learning-based approaches have shown great promise by leveraging data-driven models to detect graph properties efficiently. In particular, vision-based methods offer a visually intuitive solution by processing the visualizations of graphs. However, existing vision-based methods rely on fixed visual graph layouts, and therefore, the expressiveness of their pipeline is restricted. To overcome this limitation, we propose VSAL, a vision-based framework that incorporates an adaptive layout generator capable of dynamically producing informative graph visualizations tailored to individual instances, thereby improving graph property detection. Extensive experiments demonstrate that VSAL outperforms state-of-the-art vision-based methods on various tasks such as Hamiltonian cycle, planarity, claw-freeness, and tree detection.