The performance of graph representation learning is affected by the quality of graph input. While existing research usually pursues a globally smoothed graph embedding, we believe the rarely observed anomalies are as well harmful to an accurate prediction. This work establishes a graph learning scheme that automatically detects (locally) corrupted feature attributes and recovers robust embedding for prediction tasks. The detection operation leverages a graph autoencoder, which does not make any assumptions about the distribution of the local corruptions. It pinpoints the positions of the anomalous node attributes in an unbiased mask matrix, where robust estimations are recovered with sparsity promoting regularizer. The optimizer approaches a new embedding that is sparse in the framelet domain and conditionally close to input observations. Extensive experiments are provided to validate our proposed model can recover a robust graph representation from black-box poisoning and achieve excellent performance.

翻译：图表示学习的性能受图输入质量的影响。现有研究通常追求全局平滑的图嵌入，但我们认为罕见异常同样会损害预测的准确性。本文建立了一种图学习方案，该方案自动检测（局部）损坏的特征属性，并恢复用于预测任务的鲁棒嵌入。检测操作利用图自编码器，该方法不对局部损坏的分布做任何假设。它通过无偏掩码矩阵精确定位异常节点属性的位置，并利用稀疏促进正则化器恢复鲁棒估计。优化器得到一种在框架域中稀疏且条件接近输入观测值的新嵌入。大量实验验证了我们提出的模型能够从黑盒投毒攻击中恢复鲁棒的图表示，并实现出色的性能。