The Underappreciated Power of Vision Models for Graph Structural Understanding

• URL: http://arxiv.org/abs/2510.24788v1
• Date: Mon, 27 Oct 2025 05:11:44 GMT
• Title: The Underappreciated Power of Vision Models for Graph Structural Understanding
• Authors: Xinjian Zhao, Wei Pang, Zhongkai Xue, Xiangru Jian, Lei Zhang, Yaoyao Xu, Xiaozhuang Song, Shu Wu, Tianshu Yu,
• Abstract summary: Graph Neural Networks operate through bottom-up message-passing, fundamentally differing from human visual perception.<n>We investigate the underappreciated potential of vision models for graph understanding, finding they achieve performance comparable to GNNs on established benchmarks.
• Score: 28.781392309798672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Neural Networks operate through bottom-up message-passing, fundamentally differing from human visual perception, which intuitively captures global structures first. We investigate the underappreciated potential of vision models for graph understanding, finding they achieve performance comparable to GNNs on established benchmarks while exhibiting distinctly different learning patterns. These divergent behaviors, combined with limitations of existing benchmarks that conflate domain features with topological understanding, motivate our introduction of GraphAbstract. This benchmark evaluates models' ability to perceive global graph properties as humans do: recognizing organizational archetypes, detecting symmetry, sensing connectivity strength, and identifying critical elements. Our results reveal that vision models significantly outperform GNNs on tasks requiring holistic structural understanding and maintain generalizability across varying graph scales, while GNNs struggle with global pattern abstraction and degrade with increasing graph size. This work demonstrates that vision models possess remarkable yet underutilized capabilities for graph structural understanding, particularly for problems requiring global topological awareness and scale-invariant reasoning. These findings open new avenues to leverage this underappreciated potential for developing more effective graph foundation models for tasks dominated by holistic pattern recognition.

Related papers

• Cross-View Topology-Aware Graph Representation Learning [0.0]
  We propose GraphTCL, a dual-view contrastive learning framework that integrates structural embeddings from GNNs with topological embeddings derived from persistent homology.<n>Experiments on benchmark datasets, including TU and OGB molecular graphs, demonstrate that GraphTCL consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-12-01T19:00:58Z)
• GLANCE: Graph Logic Attention Network with Cluster Enhancement for Heterophilous Graph Representation Learning [47.674647127050186]
  Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data but often struggle on heterophilous graphs.<n>We propose GLANCE, a novel framework that integrates logic-guided reasoning, dynamic graph refinement, and adaptive clustering to enhance graph representation learning.
  arXiv  Detail & Related papers  (2025-07-24T15:45:26Z)
• 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)
• Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs [26.477872205199667]
  We introduce SCR, a unified graph reasoning framework designed to train on knowledge graphs.<n>We propose semantic-conditioned message passing, a novel mechanism addressing the inherent semantic isolation in traditional KG reasoning.<n>Our results show substantial performance gains over existing foundation models.
  arXiv  Detail & Related papers  (2024-10-16T14:26:08Z)
• Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks [0.9463895540925061]
  We introduce the semantic-structural attention-enhanced graph convolutional network (SSA-GCN) It not only models the graph structure but also extracts generalized unsupervised features to enhance classification performance. Our experiments on the Cora and CiteSeer datasets demonstrate the performance improvements achieved by our proposed method.
  arXiv  Detail & Related papers  (2024-03-24T06:28:54Z)
• On the Topology Awareness and Generalization Performance of Graph Neural Networks [6.598758004828656]
  We introduce a comprehensive framework to characterize the topology awareness of GNNs across any topological feature. We conduct a case study using the intrinsic graph metric the shortest path distance on various benchmark datasets.
  arXiv  Detail & Related papers  (2024-03-07T13:33:30Z)
• DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations [62.04558318166396]
  Graph neural networks (GNNs) demonstrate a robust capability for representation learning on graphs with complex structures. A novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced to obtain a more comprehensive embedding representation of nodes. Experimental results conducted on several graph benchmark datasets verify DGNN's superiority in node classification task.
  arXiv  Detail & Related papers  (2024-01-28T06:43:13Z)
• Graph Foundation Models: Concepts, Opportunities and Challenges [66.37994863159861]
  Foundation models have emerged as critical components in a variety of artificial intelligence applications.<n>The capabilities of foundation models in generalization and adaptation motivate graph machine learning researchers to discuss the potential of developing a new graph learning paradigm.<n>This article introduces the concept of Graph Foundation Models (GFMs), and offers an exhaustive explanation of their key characteristics and underlying technologies.
  arXiv  Detail & Related papers  (2023-10-18T09:31:21Z)
• Graph Information Bottleneck [77.21967740646784]
  Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features. Inheriting from the general Information Bottleneck (IB), GIB aims to learn the minimal sufficient representation for a given task. We show that our proposed models are more robust than state-of-the-art graph defense models.
  arXiv  Detail & Related papers  (2020-10-24T07:13:00Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

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.