GraphPFN

This is the official repository of the "GraphPFN: A Prior-Data Fitted Graph Foundation Model" ICML 2026 paper (arXiv). In this repository, we provide code for reproducing our experiments with GraphPFN, both pretraining and evaluation. See also the HuggingFace page for model weights.

News

• 2026-06-22: GraphPFN-1.3 released, featuring a refactored graph prior, native ECOC support, extended evaluation, and various other improvements!
• 2026-04-30: GraphPFN accepted to ICML 2026!
• 2026-02-13: GraphPFN-1.2 released, featuring improved ICL performance, end-to-end dataset generation and more!
• 2025-09-25: GraphPFN-1.0 released!

Licenses

• This project uses third-party components LimiX, TabICL and TabPFN. See the NOTICE file and LICENSES/ directory for details.
• GraphPFN prior in lib/graphpfn/prior is largely based on the TabICLv1 prior.
• LimiX serves as the backbone for GraphPFN, and its weights have a separate license – see the LimiX repository.

Reproducing Experiments

Prerequisites

1. Install uv
2. Install dependencies

uv sync

3. For experiments on GraphLand, download datasets and place them in the data directory

Running the evaluation

You can execute a minimal evaluation run (GraphPFN finetuning with 10 ensemble members) with the following command:

uv run bin/go.py exp/graphpfn/eval/main/finetune/10/tolokers-2/tuning.toml --force

Running the pretraining

To run GraphPFN pretraining you can use the following command:

DGLBACKEND=pytorch uv run -m torch.distributed.run --nproc-per-node 8 bin/go.py exp/graphpfn/pretrain/main/pretrain.toml

Project Structure

• bin/ - Training and evaluation scripts
• exp/ - Experiment configurations and results
• data/ - Dataset directory
• lib/ - Common utilities and tools
• vendor/ – Vendored third-party code with minor import modifications for compatibility

Configuration

Experiments are configured using TOML files located in the exp/ directory. Each configuration specifies:

• Dataset path and preprocessing
• Model hyperparameters
• Training settings
• Evaluation metrics

Results

Evaluation results are saved in the same directory as the configuration file:

• report.json – Evaluation metrics
• Model checkpoints
• Training logs