Deeper insights into graph convolutional networks for semi supervised learning

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This is a implementation of algorithms for semi-supervised classification mentioned in our paper:

See config.py for details. If you encounter any problem, please open an issue in this github page.

@inproceedings{li2018deeper,
        title = "{Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning}",
       author = {{Li}, Q. and {Han}, Z. and {Wu}, X.-M.},
    booktitle = {The Thirty-Second AAAI Conference on Artificial Intelligence},
         year = {2018},
 organization = {AAAI},
}

@article{Li2018DeeperII,
  title={Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning},
  author={Qimai Li and Zhichao Han and Xiao-Ming Wu},
  journal={ArXiv},
  year={2018},
  volume={abs/1801.07606}
}

Many interesting problems in machine learning are being revisited with new deep learning tools. [] Key Method First, we show that the graph convolution of the GCN model is actually a special form of Laplacian smoothing, which is the key reason why GCNs work, but it also brings potential concerns of over-smoothing with many convolutional layers. Second, to overcome the limits of the GCN model with shallow architectures, we propose both co-training and self-training approaches to train GCNs. Our approaches…

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