Contrastive learning (CL) has emerged as a powerful framework for learning representations of images and text in a self-supervised manner while enhancing model robustness against adversarial attacks. More recently, researchers have extended the principles of contrastive learning to graph-structured data, giving birth to the field of graph contrastive learning (GCL). However, whether GCL methods can deliver the same advantages in adversarial robustness as their counterparts in the image and text domains remains an open question. In this paper, we introduce a comprehensive robustness evaluation protocol tailored to assess the robustness of GCL models. We subject these models to adaptive adversarial attacks targeting the graph structure, specifically in the evasion scenario. We evaluate node and graph classification tasks using diverse real-world datasets and attack strategies. With our work, we aim to offer insights into the robustness of GCL methods and hope to open avenues for potential future research directions.

翻译：对比学习（CL）已成为一种强大的框架，能够以自监督方式学习图像和文本的表征，同时增强模型对对抗攻击的鲁棒性。近年来，研究者将对比学习原理扩展至图结构数据，催生了图对比学习（GCL）领域。然而，GCL方法能否在对抗鲁棒性方面展现出与图像和文本领域同等的优势仍是一个未解之谜。本文提出了一套面向GCL模型的全面鲁棒性评估协议，通过针对图结构的自适应对抗攻击（特别是在逃逸场景下）对模型进行测试。我们利用多样化的真实世界数据集与攻击策略，对节点分类和图分类任务开展评估。本研究旨在揭示GCL方法的鲁棒性特性，并为未来潜在研究方向开辟新路径。