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One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models

Research Multimodal Graph Learning

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TL;DR - OMG-VLM uses one vision-language backbone to learn across graphs with text, image, or mixed node attributes. It aims to replace modality-specific graph models while improving generalization to unseen graphs and schemas.

Structure-aware graph adapters integrate neighborhood information into the VLM’s embedding space.
A single model supports node classification and link prediction across heterogeneous attributed graphs.
Experiments report consistent gains over GNN- and LLM-based baselines.
The model demonstrates generalization to unseen graphs and varying modality configurations.

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One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models

arXiv cs.LG Jiayi Yang, Yifang Chen, Yuanfu Sun, Jiajin Liu, Qiaoyu Tan 2026-07-21 arXiv:2607.19128

Public signals Hugging Face upvotes 0

Providers: Hugging Face · Upvotes 0 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-21 14:39:02.237624 UTC

Structure-aware graph adapters integrate neighborhood information into the VLM’s embedding space.
A single model supports node classification and link prediction across heterogeneous attributed graphs.
Experiments report consistent gains over GNN- and LLM-based baselines.
The model demonstrates generalization to unseen graphs and varying modality configurations.

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