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Life sciences · Preprint
arXiv · September 3, 2026
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This is a literature survey that maps the emerging intersection of computer vision and graph neural networks for reasoning and learning about graph-structured data. It organizes existing work into three conceptual threads and identifies gaps and future directions rather than reporting original evidence or a definitive result.
Literature survey.
Survey identifies three threads: Vision for Graph Reasoning, Vision for Graph Learning, and Scientific Graphs. Authors argue that most graph learning pipelines treat graphs as purely symbolic, rarely leveraging visual form despite decades of graph visualization work.
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This is a survey paper that raises research questions and organizes existing work rather than reporting original experimental results or clinical outcomes.
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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 systematic overview of the emerging area we term vision meets graphs, which treats visual depictions of graphs as first-class inputs for reasoning and learning. We organize existing work into three threads. Vision for Graph Reasoning studies how models can use visual depictions of graphs to understand structure and carry out multi-step reasoning. Vision for Graph Learning explores how visual features can complement or augment graph encoders beyond known limitations of message passing. Scientific Graphs examines domains where standardized depiction conventions support both reasoning and learning. Our goal is to clarify what current methods can and cannot do, and to outline a path toward foundation models that perceive and reason about graphs as scientists do.
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