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

• URL: http://arxiv.org/abs/2602.13880v1
• Date: Sat, 14 Feb 2026 20:44:51 GMT
• Title: VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection
• Authors: Jiahao Xie, Guangmo Tong,
• Abstract summary: VSAL is a vision-based framework that incorporates an adaptive layout generator capable of dynamically producing graph visualizations tailored to individual instances.<n>VSAL outperforms state-of-the-art vision-based methods on various tasks such as Hamiltonian cycle, planarity, claw-freeness, and tree detection.
• Score: 10.21556794551883
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Visual Self-Refine: A Pixel-Guided Paradigm for Accurate Chart Parsing [76.2602505940467]
  Existing models often struggle with visually dense charts, leading to errors like data omission, misalignment, and hallucination.<n>Inspired by the human strategy of using a finger as a visual anchor'' to ensure accuracy when reading complex charts, we propose a new paradigm named Visual Self-Refine (VSR)<n>The core idea of VSR is to enable a model to generate pixel-level localization outputs, visualize them, and then feed these visualizations back to itself, allowing it to intuitively inspect and correct its own potential visual perception errors.
  arXiv  Detail & Related papers  (2026-02-18T13:40:53Z)
• See or Say Graphs: Agent-Driven Scalable Graph Understanding with Vision-Language Models [34.29171455515379]
  We propose a unified framework that enhances both scalability and modality coordination in graph understanding.<n>For scalability, GraphVista organizes graph information hierarchically into a lightweight GraphRAG base.<n>For modality coordination, GraphVista introduces a planning agent that routes tasks to the most suitable modality.
  arXiv  Detail & Related papers  (2025-10-19T09:20:44Z)
• Vision Graph Prompting via Semantic Low-Rank Decomposition [10.223578525761617]
  Vision GNN (ViG) demonstrates superior performance by representing images as graph structures.<n>To efficiently adapt ViG to downstream tasks, parameter-efficient fine-tuning techniques like visual prompting become increasingly essential.<n>We propose Vision Graph Prompting (VGP), a novel framework tailored for vision graph structures.
  arXiv  Detail & Related papers  (2025-05-07T04:29:29Z)
• RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning [63.599057862999]
  RefChartQA is a novel benchmark that integrates Chart Question Answering (ChartQA) with visual grounding.<n>Our experiments demonstrate that incorporating spatial awareness via grounding improves response accuracy by over 15%.
  arXiv  Detail & Related papers  (2025-03-29T15:50:08Z)
• Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements [54.006506479865344]
  We propose a unified evaluation framework for graph-level Graph Neural Networks (GNNs)<n>This framework provides a standardized setting to evaluate GNNs across diverse datasets.<n>We also propose a novel GNN model with enhanced expressivity and generalization capabilities.
  arXiv  Detail & Related papers  (2025-01-01T08:48:53Z)
• VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models [1.597617022056624]
  Large Vision-Language Models (LVLMs) are increasingly capable of tackling abstract visual tasks. We introduce VisGraphVar, a customizable benchmark generator able to produce graph images for seven task categories. We show that variations in visual attributes of images (e.g., node labeling and layout) and the deliberate inclusion of visual imperfections significantly affect model performance.
  arXiv  Detail & Related papers  (2024-11-22T10:10:53Z)
• Control-based Graph Embeddings with Data Augmentation for Contrastive Learning [3.250579305400297]
  We study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. A crucial step in contrastive learning is the creation of 'augmented' graphs from the input graphs. Here, we propose a unique method for generating these augmented graphs by leveraging the control properties of networks.
  arXiv  Detail & Related papers  (2024-03-07T22:14:04Z)
• EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning [92.71579608528907]
  This paper aims to design an easy-to-use pipeline (termed as EasyDGL) composed of three key modules with both strong ability fitting and interpretability. EasyDGL can effectively quantify the predictive power of frequency content that a model learn from the evolving graph data.
  arXiv  Detail & Related papers  (2023-03-22T06:35:08Z)
• Spectral Augmentations for Graph Contrastive Learning [50.149996923976836]
  Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learning for pre-training. We propose a set of well-motivated graph transformation operations to provide a bank of candidates when constructing augmentations for a graph contrastive objective.
  arXiv  Detail & Related papers  (2023-02-06T16:26:29Z)
• Robust Causal Graph Representation Learning against Confounding Effects [21.380907101361643]
  We propose Robust Causal Graph Representation Learning (RCGRL) to learn robust graph representations against confounding effects. RCGRL introduces an active approach to generate instrumental variables under unconditional moment restrictions, which empowers the graph representation learning model to eliminate confounders.
  arXiv  Detail & Related papers  (2022-08-18T01:31:25Z)
• GraphOpt: Learning Optimization Models of Graph Formation [72.75384705298303]
  We propose an end-to-end framework that learns an implicit model of graph structure formation and discovers an underlying optimization mechanism. The learned objective can serve as an explanation for the observed graph properties, thereby lending itself to transfer across different graphs within a domain. GraphOpt poses link formation in graphs as a sequential decision-making process and solves it using maximum entropy inverse reinforcement learning algorithm.
  arXiv  Detail & Related papers  (2020-07-07T16:51:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.