| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - graph-foundation-model |
| - graph-neural-network |
| - node-classification |
| - link-prediction |
| - transfer-learning |
| - relation-aware |
| pipeline_tag: graph-ml |
| --- |
| |
| # REEF: Relation-Aware Graph Foundation Model |
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| > 🎉 **Accepted at NeurIPS 2026.** |
| > |
| > 📄 **Paper:** [Arxiv](https://arxiv.org/abs/2505.12027v2) | 💻 **Code:** [GitHub](https://github.com/jianxiangyu/REEF/) |
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| REEF is a **graph foundation model (GFM)** that treats **relations as the fundamental transferable unit** of graphs. Each relation type (a citation link, a molecular bond, a knowledge-graph predicate, …) is described in natural language, encoded by a sentence encoder, and turned by three hypernetworks into: |
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| 1. a **relation-specific aggregator** for message passing, |
| 2. a **relation-specific classifier** for downstream prediction, and |
| 3. a **dataset-specific projector + feature bias** that adapts to different feature distributions. |
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| A single pretrained model therefore transfers across datasets, domains, and tasks (node classification, link prediction) in zero-shot and few-shot settings. |
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| ## Files in this repository |
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| | File | Size | Description | |
| |---|---|---| |
| | `reef_pretrained.pth` | ~677 MB | Pretrained `state_dict` of the `GFM` model (PyTorch pickle). | |
| | `edge_texts2id.json` | ~30 KB | Relation-text → id mapping (254 relations) used to look up relation tokens. | |
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| ## Training data |
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| Pretrained on graphs spanning four domains: |
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| - **Knowledge graphs:** FB15K237, WN18RR |
| - **Citation networks:** Citeseer, Pubmed |
| - **Web pages (heterophilous):** Texas, Wisconsin |
| - **Co-purchase:** Photo |
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| Relation descriptions are encoded with **Sentence-BERT (`all-MiniLM-L6-v2`)**. The relation vocabulary contains **254 relations** (237 from FB15K237, 11 from WN18RR, and 6 domain-level descriptions), each with an LLM-generated textual description. |
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| ## Citation |
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| ```bibtex |
| @article{yu2025relation, |
| title={Relation-Aware Graph Foundation Model}, |
| author={Yu, Jianxiang and Zhu, Jiapeng and Qian, Hao and Liu, Ziqi and Zhang, Zhiqiang and Li, Xiang}, |
| journal={arXiv preprint arXiv:2505.12027}, |
| year={2025} |
| } |
| ``` |
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