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Virtual Adversarial Training on Graph Convolutional Networks in Node Classification

By Ke Sun and others
The effectiveness of Graph Convolutional Networks (GCNs) has been demonstrated in a wide range of graph-based machine learning tasks. However, the update of parameters in GCNs is only from labeled nodes, lacking the utilization of unlabeled data. In this paper, we apply Virtual Adversarial Training (VAT), an adversarial regularization method... Show more
February 20, 2020
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Virtual Adversarial Training on Graph Convolutional Networks in Node Classification
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