GraphGPT: Generative Pre-trained Graph Eulerian Transformer

Qifang Zhao, Weidong Ren, Tianyu Li, Hong Liu, Xingsheng He, Xiaoxiao Xu
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:77630-77656, 2025.

Abstract

We introduce GraphGPT, a novel self-supervised generative pre-trained model for graph learning based on the Graph Eulerian Transformer (GET). First, we propose GET, which combines a standard transformer encoder or decoder architecture with an innovative graph-to-sequence transformation method. This method converts graphs or sampled subgraphs into sequences of tokens representing nodes, edges, and attributes in a reversible manner using Eulerian paths. We pre-train GET using either of the two self-supervised tasks: next-token prediction (NTP) and scheduled masked-token prediction (SMTP). The pre-trained model is then fine-tuned for downstream tasks such as graph-, edge-, and node-level prediction. Despite its simplicity, GraphGPT achieves performance comparable to or surpassing state-of-the-art methods on multiple large-scale Open Graph Benchmark (OGB) datasets. It demonstrates exceptional results on the molecular property prediction dataset PCQM4Mv2 and the protein-protein interaction dataset ogbl-ppa. Notably, generative pre-training enables scaling GraphGPT to 2 billion parameters while maintaining performance gains — a breakthrough that overcomes the scalability limitations of traditional Graph Neural Networks (GNNs) and prior graph transformers (GTs). To advance research in graph foundation models and facilitate scientific discovery in chemistry, materials science, and related fields, we have released the source code (https://github.com/alibaba/graph-gpt) and model checkpoints (https://www.modelscope.cn/organization/Alibaba-DT).

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-zhao25r, title = {{G}raph{GPT}: Generative Pre-trained Graph {E}ulerian Transformer}, author = {Zhao, Qifang and Ren, Weidong and Li, Tianyu and Liu, Hong and He, Xingsheng and Xu, Xiaoxiao}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {77630--77656}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/zhao25r/zhao25r.pdf}, url = {https://proceedings.mlr.press/v267/zhao25r.html}, abstract = {We introduce GraphGPT, a novel self-supervised generative pre-trained model for graph learning based on the Graph Eulerian Transformer (GET). First, we propose GET, which combines a standard transformer encoder or decoder architecture with an innovative graph-to-sequence transformation method. This method converts graphs or sampled subgraphs into sequences of tokens representing nodes, edges, and attributes in a reversible manner using Eulerian paths. We pre-train GET using either of the two self-supervised tasks: next-token prediction (NTP) and scheduled masked-token prediction (SMTP). The pre-trained model is then fine-tuned for downstream tasks such as graph-, edge-, and node-level prediction. Despite its simplicity, GraphGPT achieves performance comparable to or surpassing state-of-the-art methods on multiple large-scale Open Graph Benchmark (OGB) datasets. It demonstrates exceptional results on the molecular property prediction dataset PCQM4Mv2 and the protein-protein interaction dataset ogbl-ppa. Notably, generative pre-training enables scaling GraphGPT to 2 billion parameters while maintaining performance gains — a breakthrough that overcomes the scalability limitations of traditional Graph Neural Networks (GNNs) and prior graph transformers (GTs). To advance research in graph foundation models and facilitate scientific discovery in chemistry, materials science, and related fields, we have released the source code (https://github.com/alibaba/graph-gpt) and model checkpoints (https://www.modelscope.cn/organization/Alibaba-DT).} }
Endnote
%0 Conference Paper %T GraphGPT: Generative Pre-trained Graph Eulerian Transformer %A Qifang Zhao %A Weidong Ren %A Tianyu Li %A Hong Liu %A Xingsheng He %A Xiaoxiao Xu %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-zhao25r %I PMLR %P 77630--77656 %U https://proceedings.mlr.press/v267/zhao25r.html %V 267 %X We introduce GraphGPT, a novel self-supervised generative pre-trained model for graph learning based on the Graph Eulerian Transformer (GET). First, we propose GET, which combines a standard transformer encoder or decoder architecture with an innovative graph-to-sequence transformation method. This method converts graphs or sampled subgraphs into sequences of tokens representing nodes, edges, and attributes in a reversible manner using Eulerian paths. We pre-train GET using either of the two self-supervised tasks: next-token prediction (NTP) and scheduled masked-token prediction (SMTP). The pre-trained model is then fine-tuned for downstream tasks such as graph-, edge-, and node-level prediction. Despite its simplicity, GraphGPT achieves performance comparable to or surpassing state-of-the-art methods on multiple large-scale Open Graph Benchmark (OGB) datasets. It demonstrates exceptional results on the molecular property prediction dataset PCQM4Mv2 and the protein-protein interaction dataset ogbl-ppa. Notably, generative pre-training enables scaling GraphGPT to 2 billion parameters while maintaining performance gains — a breakthrough that overcomes the scalability limitations of traditional Graph Neural Networks (GNNs) and prior graph transformers (GTs). To advance research in graph foundation models and facilitate scientific discovery in chemistry, materials science, and related fields, we have released the source code (https://github.com/alibaba/graph-gpt) and model checkpoints (https://www.modelscope.cn/organization/Alibaba-DT).
APA
Zhao, Q., Ren, W., Li, T., Liu, H., He, X. & Xu, X.. (2025). GraphGPT: Generative Pre-trained Graph Eulerian Transformer. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:77630-77656 Available from https://proceedings.mlr.press/v267/zhao25r.html.

Related Material