PyTorch implementation of Progressive Graph Convolutional Networks for Semi-Supervised Node Classification method [1]. This work is an extension of GCN method [2], which finds an optimized network GCN architecture for semi-supervised node classification.
The preprocessed Citeseer, Cora and Pubmed datasets can be found in ./pgcn/data directory.
python setup.py install
- PyTorch 0.4 or 0.5
- Python 2.7 or 3.6
git clone https://github.com/negarhdr/PGCN
python setup.py install
cd pgcn
python pgcn.py --dataset cora --blocksize 5 --epochs 200
You can specify different values for the model's hyper-parameters such as blocksize, number of epochs, dataset name, etc.
[2] Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016
This repo is modified based on PyGCN.
Please cite our paper if you use this code in your work:
@article{heidari2020progressive,
title={Progressive graph convolutional networks for semi-supervised node classification},
author={Heidari, Negar and Iosifidis, Alexandros},
journal={arXiv preprint arXiv:2003.12277},
year={2020}
}