This repository is the official implementation of Random Walk Graph Neural Networks.
Code is written in Python 3.6 and requires:
- PyTorch 1.5
- scikit-learn 0.21
Use the following link to download datasets:
https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets
Extract the datasets into the datasets folder.
To train and evaluate the model in the paper, run this command:
python main.py --dataset <dataset_name>
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
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):
| Model name | MUTAG | D&D | NCI1 | PROTEINS | ENZYMES |
|---|---|---|---|---|---|
| SP | 80.2 (± 6.5) | 78.1 (± 4.1) | 72.7 (± 1.4) | 75.3 (± 3.8) | 38.3 (± 8.0) |
| GR | 80.8 (± 6.4) | 75.4 (± 3.4) | 61.8 (± 1.7) | 71.6 (± 3.1) | 25.1 (± 4.4) |
| WL | 84.6 (± 8.3) | 78.1 (± 2.4) | 84.8 (± 2.5) | 73.8 (± 4.4) | 50.3 (± 5.7) |
| DGCNN | 84.0 (± 6.7) | 76.6 (± 4.3) | 76.4 (± 1.7) | 72.9 (± 3.5) | 38.9 (± 5.7) |
| DiffPool | 79.8 (± 7.1) | 75.0 (± 3.5) | 76.9 (± 1.9) | 73.7 (± 3.5) | 59.5 (± 5.6) |
| ECC | 75.4 (± 6.2) | 72.6 (± 4.1) | 76.2 (± 1.4) | 72.3 (± 3.4) | 29.5 (± 8.2) |
| GIN | 84.7 (± 6.7) | 75.3 (± 2.9) | 80.0 (± 1.4) | 73.3 (± 4.0) | 59.6 (± 4.5) |
| GraphSAGE | 83.6 (± 9.6) | 72.9 (± 2.0) | 76.0 (± 1.8) | 73.0 (± 4.5) | 58.2 (± 6.0) |
| 1-step RWNN | 89.2 (± 4.3) | 77.6 (± 4.7) | 71.4 (± 1.8) | 74.7 (± 3.3) | 56.7 (± 5.2) |
| 2-step RWNN | 88.1 (± 4.8) | 76.9 (± 4.6) | 73.0 (± 2.0) | 74.1 (± 2.8) | 57.4 (± 4.9) |
| 3-step RWNN | 88.6 (± 4.1) | 77.4 (± 4.9) | 73.9 (± 1.3) | 74.3 (± 3.3) | 57.6 (± 6.3) |
| Model name | IMDB-BINARY | IMDB-MULTI | REDDIT-BINARY | REDDIT-MULTI-5K | COLLAB |
|---|---|---|---|---|---|
| SP | 57.7 (± 4.1) | 39.8 (± 3.7) | 89.0 (± 1.0) | 51.1 (± 2.2) | 79.9 (± 2.7) |
| GR | 63.3 (± 2.7) | 39.6 (± 3.0) | 76.6 (± 3.3) | 38.1 (± 2.3) | 71.1 (± 1.4) |
| WL | 72.8 (± 4.5) | 51.2 (± 6.5) | 74.9 (± 1.8) | 49.6 (± 2.0) | 78.0 (± 2.0) |
| DGCNN | 69.2 (± 3.0) | 45.6 (± 3.4) | 87.8 (± 2.5) | 49.2 (± 1.2) | 71.2 (± 1.9) |
| DiffPool | 68.4 (± 3.3) | 45.6 (± 3.4) | 89.1 (± 1.6) | 53.8 (± 1.4) | 68.9 (± 2.0) |
| ECC | 67.7 (± 2.8) | 43.5 (± 3.1) | OOR | OOR | OOR |
| GIN | 71.2 (± 3.9) | 48.5 (± 3.3) | 89.9 (± 1.9) | 56.1 (± 1.7) | 75.6 (± 2.3) |
| GraphSAGE | 68.8 (± 4.5) | 47.6 (± 3.5) | 84.3 (± 1.9) | 50.0 (± 1.3) | 73.9 (± 1.7) |
| 1-step RWNN | 70.8 (± 4.8) | 47.8 (± 3.8) | 90.4 (± 1.9) | 51.7 (± 1.5) | 71.7 (± 2.1) |
| 2-step RWNN | 70.6 (± 4.4) | 48.8 (± 2.9) | 90.3 (± 1.8) | 51.7 (± 1.4) | 71.3 (± 2.1) |
| 3-step RWNN | 70.7 (± 3.9) | 47.8 (± 3.5) | 89.7 (± 1.2) | 53.4 (± 1.6) | 71.9 (± 2.5) |
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}
}