让图像化图分析更智能：动态生成最佳布局提升属性识别准确率
VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection
- 引入自适应布局生成器，按图实例动态优化可视化
- 在哈密顿环、平面性等任务上超越现有视觉方法
- 适合需要高精度图结构分析的研究者与工程师
图属性检测旨在判断图是否具备特定结构特性，如是否存在哈密顿环。近年来，基于学习的方法通过数据驱动模型实现了高效检测。特别是视觉方法将图可视化后处理，具有直观优势。然而，现有方法依赖固定布局，限制了表达能力。为此，本文提出VSAL，一种融合自适应布局生成器的视觉框架，能为每个图实例动态生成更具信息量的可视化表示，从而提升检测性能。大量实验表明，VSAL在哈密顿环、平面性、无爪性及树检测等多项任务中均优于当前最先进的视觉方法。
原文摘要 · Abstract (English)
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.
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