带信息正则化的对抗图对比学习
Adversarial Graph Contrastive Learning with Information Regularization
AI总结：
针对图数据增强难以产生高质量对比样本的问题，本文提出ARIEL，利用对抗图视图与信息正则化在约束内生成信息性样本，提升节点分类性能与鲁棒性。
摘要：
对比学习是图表示学习中一种有效的无监督方法。近来，基于数据增强的对比学习方法已从图像扩展到图。然而，以往大多数工作都直接改编自为图像设计的模型。与图像上的数据增强不同，图上的数据增强远没有那么直观，也更难提供高质量的对比样本，而这些样本正是对比学习模型性能的关键。这为改进现有图对比学习框架留下了很大空间。在本工作中，通过引入对抗图视图和信息正则化器，我们提出了一种简单但有效的方法——对抗图对比学习（Adversarial Graph Contrastive Learning，ARIEL），以在合理约束内提取信息丰富的对比样本。在多个真实世界数据集的节点分类任务中，该方法始终优于当前的图对比学习方法，并进一步提升了图对比学习的鲁棒性。代码位于 https://github.com/Shengyu-Feng/ARIEL。
英文摘要：
Contrastive learning is an effective unsupervised method in graph representation learning. Recently, the data augmentation based contrastive learning method has been extended from images to graphs. However, most prior works are directly adapted from the models designed for images. Unlike the data augmentation on images, the data augmentation on graphs is far less intuitive and much harder to provide high-quality contrastive samples, which are the key to the performance of contrastive learning models. This leaves much space for improvement over the existing graph contrastive learning frameworks. In this work, by introducing an adversarial graph view and an information regularizer, we propose a simple but effective method, Adversarial Graph Contrastive Learning (ARIEL), to extract informative contrastive samples within a reasonable constraint. It consistently outperforms the current graph contrastive learning methods in the node classification task over various real-world datasets and further improves the robustness of graph contrastive learning. The code is at https://github.com/Shengyu-Feng/ARIEL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
