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On Generalization of Graph Autoencoders with Adversarial Training

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 نشر من قبل Tianjin Huang
 تاريخ النشر 2021
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Adversarial training is an approach for increasing models resilience against adversarial perturbations. Such approaches have been demonstrated to result in models with feature representations that generalize better. However, limited works have been done on adversarial training of models on graph data. In this paper, we raise such a question { does adversarial training improve the generalization of graph representations. We formulate L2 and



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