gcn

Graph Convolutional Networks (GCN)

• Paper link: https://arxiv.org/abs/1609.02907
• Author's code repo: https://github.com/tkipf/gcn. Note that the original code is implemented with Tensorflow for the paper.

Dependencies

• Tensorflow 2.1+
• requests

bash pip install tensorflow requests export DGLBACKEND=tensorflow

Codes

The folder contains three implementations of GCN:

• gcn.py uses DGL's predefined graph convolution module.
• gcn_mp.py uses user-defined message and reduce functions.
• gcn_builtin.py improves from gcn_mp.py by using DGL's builtin functions so SPMV optimization could be applied.

Results

Run with following (available dataset: "cora", "citeseer", "pubmed")

python3 train.py --dataset cora --gpu 0 --self-loop

• cora: ~0.810 (0.79-0.83) (paper: 0.815)
• citeseer: 0.707 (paper: 0.703)
• pubmed: 0.792 (paper: 0.790)