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PGCN

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.

Graph Convolutional Networks

The preprocessed Citeseer, Cora and Pubmed datasets can be found in ./pgcn/data directory.

Installation

python setup.py install

Requirements

  • PyTorch 0.4 or 0.5
  • Python 2.7 or 3.6

Running the code

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.

References

[1] Heidari & Iosifidis, Progressive Graph Convolutional Networks for Semi-Supervised Node Classification, 2020

[2] Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016

Acknowledgement

This repo is modified based on PyGCN.

Cite

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}
}

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PyTorch implementation of PGCN algorithm.

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