Random Walk Graph Neural Networks

Last update: Jan 2, 2023

Overview

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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Comments

are you interested in freelancing

@giannisnik hi, i have a task that can be solved using GNNs, are you interested in freelancing. if so, then what is your email so i can share furthur information. waiting for your reply

opened by seekingdeep 0

Random Walk Graph Neural Networks

Related tags

Overview

Random Walk Graph Neural Networks

Requirements

Datasets

Training and Evaluation

Example

Results

Cite

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Comments

are you interested in freelancing

Owner

Giannis Nikolentzos

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