This repository contains a Tensorflow 2.0 implementation of the experiments in the following paper:
Keyulu Xu*, Weihua Hu*, Jure Leskovec, Stefanie Jegelka. How Powerful are Graph Neural Networks? ICLR 2019.
If you make use of the code/experiment or GIN algorithm in your work, please cite their paper (Bibtex below).
@inproceedings{
xu2018how,
title={How Powerful are Graph Neural Networks?},
author={Keyulu Xu and Weihua Hu and Jure Leskovec and Stefanie Jegelka},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=ryGs6iA5Km},
}
I created this Tensorflow 2.0 implementation for educational purposes, and to further my knowledge in this domain.
Run Tensorflow_2_0_Graph_Isomorphism_Networks_(GIN).ipynb
In the colab sheet, you can choose to run different datasets and modify the training properties of the Graph Isomorphism Networks
Install Tensorflow 2.0
Then install the other dependencies.
pip install -r requirements.txt
Unzip the dataset file
unzip dataset.zip
and run
python main.py
Note: Default parameters are not the best performing-hyper-parameters. Please refer to the above paper for the details of how the researchers above set their hyper-parameters.