SOURCE-LINKED INTELLIGENCE
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 a
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T13:19:22.000Z
First collected: 2026-09-21T04:51:57.792Z. This is not the publication date.