Graph Adversarial Training：Dynamically Regularizing Based on Graph Structure.pdf

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文件名称：Graph Adversarial Training：Dynamically Regularizing Based on Graph Structure.pdf
文件大小：1.83MB
文件格式：PDF
更新时间：2022-08-29 08:54:36
KG Abstract—Recent efforts show that neural networks are vulnerable to small but intentional perturbations on input features in visual classification tasks. Due to the additional consideration of connections between examples (e.g., articles with citation link tend to be in the same class), graph neural networks could be more sensitive to the perturbations, since the perturbations from connected examples exacerbate the impact on a target example. Adversarial Training (AT), a dynamic regularization technique, can resist the worst-case perturbations on input features and is a promising choice to improve model robustness and generalization. However, existing AT methods focus on standard classification, being less effective when training models on graph since it does not model the impact from connected examples.