Random Walk Graph Neural Networks

This repository is the official implementation of Random Walk Graph Neural Networks.

Requirements

Code is written in Python 3.6 and requires:

• PyTorch 1.5
• scikit-learn 0.21

Datasets

Use the following link to download datasets:

https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets

Extract the datasets into the datasets folder.

Training and Evaluation

To train and evaluate the model in the paper, run this command:

python main.py --dataset <dataset_name> 

Example

To train and evaluate the model on MUTAG, first specify the hyperparameters in the main.py file and then run:

python main.py --dataset MUTAG --use-node-labels

Results

Our model achieves the following performance on standard graph classification datasets (note that we used the evaluation procedure and same data splits as in this paper):

Cite

Please cite our paper if you use this code:

@inproceedings{nikolentzos2020random,
  title={Random Walk Graph Neural Networks},
  author={Nikolentzos, Giannis and Vazirgiannis, Michalis},
  booktitle={Proceedings of the 34th Conference on Neural Information Processing Systems},
  pages={16211--16222},
  year={2020}
}