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20 hours ago •
giannisnik
giannisnik
Random Walk Graph Neural Networks
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):
| 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) |
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}
}
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