GraphBench: Next-generation graph learning benchmarking

This is the package associated with the paper GraphBench: Next generation graph learning benchmarking.

It contains the code and tools needed to load the benchmark's datasets. GraphBench is a collection of benchmarking datasets across domains and tasks obtained from real-world and synthetic applications.

Features

GraphBench comes as a Python package with the following features:

Installation

We recommend using Anaconda/Miniconda during setup. The installation process is done in two steps:

1. Install swig via Anaconda/Miniconda (if using the optimization module): conda install swig

2. GraphBench can then be easily installed using the Python package manager pip: pip install graphbench-lib

For using the optimization module graphbench-lib[tuning], it needs to be installed instead.

Please make sure to install GraphBench before running the benchmark for the best results. Alternatively, one can also install from source:

git clone https://github.com/graphbench/package
cd package
pip install -e . 

Dependencies

GraphBench uses several Python packages to load and process datasets. An overview of the required packages can be found in the list below:

• torch
• torch_geometric
• networkx
• torchmetrics
• numpy
• pandas
• requests
• tqdm
• scikit-learn

Optionally, SMAC3 is used for the optimization module.

Usage

The package can be easily used to get selected datasets from the GraphBench tasks:

import graphbench
Loader = graphbench.Loader(root, dataset_name)
datasets = Loader.load()

Furthermore, standardized evaluation metrics can be obtained using the following methods:

Evaluator = graphbench.Evaluator(metric_name)
metric_results = Evaluator.evaluate()

In order to use all datasets of a domain easily, each domain corresponds to one dataset_name variable:

Note that for algorithmic reasoning, the download always includes all datasets for a given task. We plan to change this in the future.

For a complete list of the datasets, see the accompanying website or the datasets.csv file. The corresponding metrics are in the master.csv file.

Citing GraphBench:

If you use GraphBench or GraphBench datasets in your work, please cite our paper:

@article{GraphBench,
title={GraphBench: Next-generation graph learning benchmarking}, 
author={Timo Stoll and Chendi Qian and Ben Finkelshtein and Ali Parviz and Darius Weber and Fabrizio Frasca and Hadar Shavit and Antoine Siraudin and Arman Mielke and Marie Anastacio and Erik Müller and Maya Bechler-Speicher and Michael Bronstein and Mikhail Galkin and Holger Hoos and Mathias Niepert and Bryan Perozzi and Jan Tönshoff and Christopher Morris},
year={2025},
journal={arXiv preprint arXiv:2512.04475}
}