Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models

• URL: http://arxiv.org/abs/2503.21435v2
• Date: Mon, 26 May 2025 16:31:06 GMT
• Title: Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models
• Authors: Ruizhou Li, Haiyun Jiang,
• Abstract summary: We introduce the first comprehensive benchmark designed to evaluate and enhance the multi-graph reasoning abilities of Vision-Language Models (VLMs)<n>Our benchmark covers four common graph types-knowledge graphs, flowcharts, mind maps, and route maps-and supports both homogeneous and heterogeneous graph groupings.<n>We evaluate several state-of-the-art VLMs under a multi-dimensional scoring framework that assesses graph parsing, reasoning consistency, and instruction-following accuracy.
• Score: 10.813015912529936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in Vision-Language Models (VLMs) have shown promising capabilities in interpreting visualized graph data, offering a new perspective for graph-structured reasoning beyond traditional Graph Neural Networks (GNNs). However, existing studies focus primarily on single-graph reasoning, leaving the critical challenge of multi-graph joint reasoning underexplored. In this work, we introduce the first comprehensive benchmark designed to evaluate and enhance the multi-graph reasoning abilities of VLMs. Our benchmark covers four common graph types-knowledge graphs, flowcharts, mind maps, and route maps-and supports both homogeneous and heterogeneous graph groupings with tasks of increasing complexity. We evaluate several state-of-the-art VLMs under a multi-dimensional scoring framework that assesses graph parsing, reasoning consistency, and instruction-following accuracy. Additionally, we fine-tune multiple open-source models and observe consistent improvements, confirming the effectiveness of our dataset. This work provides a principled step toward advancing multi-graph understanding and reveals new opportunities for cross-modal graph intelligence.

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