![[2602.13880] VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13880] VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection
Summary
The paper presents VSAL, a vision-based framework for graph property detection that utilizes adaptive layouts to enhance the detection of structural properties in graphs, outperforming existing methods.
Why It Matters
Graph property detection is crucial in various fields, including computer science and network analysis. The introduction of adaptive layouts in VSAL enhances the efficiency and accuracy of detecting properties like Hamiltonian cycles, making it a significant advancement in the application of machine learning to graph theory.
Key Takeaways
- VSAL introduces an adaptive layout generator for graph visualizations.
- The framework improves the detection of graph properties such as Hamiltonian cycles and planarity.
- Extensive experiments show VSAL outperforms existing vision-based methods.
- Adaptive layouts allow for more informative visual representations tailored to specific graph instances.
- The research highlights the potential of vision-based approaches in graph property detection.
Computer Science > Artificial Intelligence arXiv:2602.13880 (cs) [Submitted on 14 Feb 2026] Title:VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection Authors:Jiahao Xie, Guangmo Tong View a PDF of the paper titled VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection, by Jiahao Xie and 1 other authors View PDF 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. Comments: Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2602.13880 [cs.AI] (or arXiv:2602.13880v1 [cs.AI] for ...
