GraphVLM：面向多模态图学习的视觉语言模型基准测试
GraphVLM: Benchmarking Vision Language Models for Multimodal Graph Learning
该基准系统评估了VLM作为编码器、对齐器和预测器三种范式在多模态图学习中的表现，为视觉研究者将VLM与图结构数据结合提供了可复用的评测框架和范式参考。
原文依据 we present GraphVLM, a systematic benchmark designed to evaluate and harness the capabilities of VLMs for multimodal graph learning (MMGL).
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉语言模型（VLM）在对齐和理解多模态信号方面已展现出显著能力，但其在结构化数据（其中多模态实体通过显式关系图连接）上进行推理的潜力仍很大程度上未被充分探索。解锁这一能力对于社交网络、推荐系统和科学发现等现实应用至关重要，因为这些应用中的多模态信息本质上是结构化的。为弥合这一差距，我们提出了GraphVLM，一个系统性的基准，旨在评估和利用VLM进行多模态图学习（MMGL）的能力。GraphVLM研究了将VLM与图推理集成的三种互补范式：（1）VLM作为编码器，通过多模态特征融合增强图神经网络；（2）VLM作为对齐器，在潜变量或语言空间中桥接模态，以促进基于LLM的结构化推理；（3）VLM作为预测器，直接采用VLM作为多模态骨干进行图学习任务。在来自不同领域的六个数据集上进行的大量实验表明，VLM通过所有三种角色增强了多模态图学习。在这些范式中，VLM作为预测器取得了最显著且一致的性能提升，揭示了视觉语言模型作为多模态图学习新基础的未开发潜力。
英文原文摘要 1401 字符
Vision-Language Models (VLMs) have demonstrated remarkable capabilities in aligning and understanding multimodal signals, yet their potential to reason over structured data, where multimodal entities are connected through explicit relational graphs, remains largely underexplored. Unlocking this capability is crucial for real-world applications such as social networks, recommendation systems, and scientific discovery, where multimodal information is inherently structured. To bridge this gap, we present GraphVLM, a systematic benchmark designed to evaluate and harness the capabilities of VLMs for multimodal graph learning (MMGL). GraphVLM investigates three complementary paradigms for integrating VLMs with graph reasoning: (1) VLM-as-Encoder, which enriches graph neural networks through multimodal feature fusion; (2) VLM-as-Aligner, which bridges modalities in latent or linguistic space to facilitate LLM-based structured reasoning; and (3) VLM-as-Predictor, which directly employs VLMs as multimodal backbones for graph learning tasks. Extensive experiments across six datasets from diverse domains demonstrate that VLMs enhance multimodal graph learning via all three roles. Among these paradigms, VLM-as-Predictor achieves the most substantial and consistent performance gains, revealing the untapped potential of vision-language models as a new foundation for multimodal graph learning.