# Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning

@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… Expand

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