ipark2021/tudataset

TUDataset

Source code for our ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020) paper "TUDataset: A collection of benchmark datasets for learning with graphs". This repository contains graph kernel and GNN baseline implementations, data loaders, and evaluations scripts.

See graphlearning.io for documentation on how to use this package.

Installation

First make sure that you have all requirements installed.

Requirements

• networkx
• numpy (somewhat recent version)
• pandas (somewhat recent version)
• Python 3.x
• scipy (somewhat recent version)
• sklearn (somewhat recent version)
• torch 1.5
• torch-geometric 1.5

If you want to use the kernel baselines, you will further need:

• eigen3
• g++
• pybind11

Compilation of kernel baselines

To execute the kernel baselines you have to compile the Python package. If you just want to use the GNN baselines, data loaders, and evaluation scripts, you can skip this step.

Execute the following steps:

$ cd tud_benchmark/kernel_baselines

If you are using a Linux system, run

$ g++ -O3 -shared -std=c++11 -fPIC `python3 -m pybind11 --includes`  kernel_baselines.cpp src/*cpp -o ../kernel_baselines`python3-config --extension-suffix`

on MacOS, run

$ g++ -O3 -shared -std=c++11 -undefined dynamic_lookup `python3 -m pybind11 --includes`  kernel_baselines.cpp src/*cpp -o ../kernel_baselines`python3-config --extension-suffix`

You might need to adjust your path to pybind11 and eigen3 in kernel_baselines.cpp, kernel_baselines/src/AuxiliaryMethods.h, and kernel_baselines/src/Graph.cpp.

Usage

See tud_benchmark/main_kernel.py and tud_benchmark/main_gnn.py for baseline and evaluation examples. More details can be found at graphlearning.io.

Terms and conditions

Please feel free to use our code. We only ask that you cite:

@inproceedings{Morris+2020,
    title={TUDataset: A collection of benchmark datasets for learning with graphs},
    author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
    booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
    pages={},
    url={www.graphlearning.io}
    year={2020}
}

Contact Information

If you have any questions, send an email to Christopher Morris (christopher.morris at tu-dortmund.de).