dlwbm123 / rwgnn

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}
}