PyTorch and TensorFlow2 implementation of Snowball and Truncated Krylov Graph Convolutional Network (GCN) architectures for semi-supervised classification [1].

This repository contains the Cora, CiteSeer and PubMed dataset.

Results are collected through the PyTorch implementation. These results WILL BE UPDATED since we have greatly optimized the implementations.

There are slight differences between the 2 implementations, so you may have to redo the hyperparameter search for the TensorFlow2 implementation.

Please feel free to leave comments if you have trouble reproducing the results!

• PyTorch 1.3.x or TensorFlow 2.x.x
• Python 3.6+
• Best with NVIDIA apex (we have used the NGC container with singularity)

python initialize_dataset.py

python train.py

[1] Luan, et al., Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks, 2019

Please kindly cite our work if necessary:

@incollection{luan2019break,
title = {Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks},
author = {Luan, Sitao and Zhao, Mingde and Chang, Xiao-Wen and Precup, Doina},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {10943-10953},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {https://arxiv.org/abs/1906.02174}
}