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

• URL: http://arxiv.org/abs/2503.21435v1
• Date: Thu, 27 Mar 2025 12:20:37 GMT
• Title: Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models
• Authors: Ruizhou Li, Haiyun Jiang,
• Abstract summary: Vision-Language Models (VLMs) have demonstrated exceptional cross-modal relational reasoning capabilities and generalization capacities.<n>Our benchmark encompasses four graph categories: knowledge graphs, flowcharts, mind maps, and route maps, with each graph group accompanied by three progressively challenging instruction-response pairs.<n>This study not only addresses the underexplored evaluation gap in multi-graph reasoning for VLMs but also empirically validates their generalization superiority in graph-structured learning.
• Score: 10.813015912529936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs), as the dominant paradigm for graph-structured learning, have long faced dual challenges of exponentially escalating computational complexity and inadequate cross-scenario generalization capability. With the rapid advancement of multimodal learning, Vision-Language Models (VLMs) have demonstrated exceptional cross-modal relational reasoning capabilities and generalization capacities, thereby opening up novel pathways for overcoming the inherent limitations of conventional graph learning paradigms. However, current research predominantly concentrates on investigating the single-graph reasoning capabilities of VLMs, which fundamentally fails to address the critical requirement for coordinated reasoning across multiple heterogeneous graph data in real-world application scenarios. To address these limitations, we propose the first multi-graph joint reasoning benchmark for VLMs. Our benchmark encompasses four graph categories: knowledge graphs, flowcharts, mind maps, and route maps,with each graph group accompanied by three progressively challenging instruction-response pairs. Leveraging this benchmark, we conducted comprehensive capability assessments of state-of-the-art VLMs and performed fine-tuning on open-source models. This study not only addresses the underexplored evaluation gap in multi-graph reasoning for VLMs but also empirically validates their generalization superiority in graph-structured learning.

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