GCN-pytorch

This is my pytorch's implementation of tkipf's GCN.

For the original version: Tensorflow-Version

There are minor differences, including:

1. Add multiheads to each layers
2. Dropout is applied not only to the input of each layer, but also to the symmetrically normalized Laplacian matrix in each head
3. We leverage a GAT style early stop mechanism, which can greatly enhance the performance

For any questions, please feel free to open an issue. You may expect my response in less than 7 days.

Future Works

1. Add argparse to the code to make it easier to try different hyperparameters
2. Tuning the model for a better performance on Cora
3. Might add some other aggregation based model to this repository.

Notification

It should be notified that I am not the author of GCN. You might cite this work according to Tensorflow-Version.

Performance

This model could achieve an average accuracy tested on Cora at

How to run our code

Requirements

Before running our codes, please make sure these dependencies is installed in your environment

1. numpy==1.16.4
2. torch==1.1.0
3. scipy==1.3.0
4. networkx==2.3

by

python -m pip install -r requirements.txt

Evaluating on Cora, Citeseer, Pubmed

You may run

python gcn_main.py [-h] [-dataset [DATASET]] [-epoch EPOCH] [-early EARLY]
                   [-device [DEVICE]] [-lr LR] [-weight WEIGHT]
                   [-hidden [HIDDEN]] [-layer LAYER]

• -dataset you should use one of 'cora', 'pubmed', 'citeseer'. Default is cora
• -epoch maximum epoch, default is 1000
• -early early stop, default is 100
• -device which devices used to train, default is cpu
• -lr learning rate, default is 0.01
• -weight weight decay, default is 5e-4
• -hidden hidden layer size, split by comma, default is 16
• -layer layer number, default is 2