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

ArXi:2503.21435v3 Announce Type: replace 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