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.

翻译：图属性检测旨在判定一个图是否具有特定结构属性，例如是否为哈密顿图。近年来，基于学习的方法通过利用数据驱动模型高效检测图属性，展现出巨大潜力。特别是基于视觉的方法通过处理图的可视化表示，提供了一种视觉直观的解决方案。然而，现有的基于视觉的方法依赖于固定的视觉图布局，因此其流程的表达能力受到限制。为克服这一局限，我们提出了VSAL——一种基于视觉的框架，它集成了一个自适应布局生成器，能够针对单个图实例动态生成信息丰富的图可视化，从而提升图属性检测性能。大量实验表明，VSAL在哈密顿环检测、平面性检测、无爪图检测及树检测等多种任务上均优于当前最先进的基于视觉的方法。