When Vision Meets Graphs: A Survey on Graph Reasoning and Learning
Graphs are a fundamental data structure underlying many problems in the natural and social sciences. Over the past decade, Graph Neural Networks (GNNs) have dominated graph machine learning, supported by solid theoretical foundations. Yet scientists often understand graph structure through vision: chemists read molecular diagrams and social scientists inspect network visualizations. Despite decades of work on graph visualization, most graph learning pipelines still treat graphs purely as symbolic structures, rarely leveraging the visual form of graphs. We argue that this gap deserves renewed attention in the era of powerful vision and vision-language models. This survey provides a first s...
| Authors | Xinjian Zhao, Wei Pang, Zhixuan Yu et al. |
|---|---|
| Primary category | cs.SI |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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