论文标题
图表上对抗性学习的调查
A Survey of Adversarial Learning on Graphs
论文作者
论文摘要
图形上的深度学习模型在各种图形分析任务中取得了显着的性能，例如节点分类，链接预测和图形群集。但是，它们暴露了针对精心设计的投入的不确定性和不可靠性，即对抗性示例。因此，在不同的图形分析任务中解决的攻击和防御方面已经出现了一系列研究，从而导致了对抗性学习的武器竞赛。尽管蓬勃发展，但仍然缺乏统一的问题定义和全面的审查。为了弥合这一差距，我们系统地调查并总结了现有的图形对手学习任务的现有作品。具体而言，我们调查并统一了现有作品W.R.T.图形分析任务中的攻击和防御，并同时提供适当的定义和分类法。此外，我们强调相关评估指标的重要性，全面调查和总结它们。希望我们的作品可以为相关研究人员提供全面的概述，并为相关研究人员提供见解。图形对抗学习的最新进展总结在我们的github存储库中https://github.com/edisonleeeee/graph-versarial-learning。
Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction, and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, a line of studies has emerged for both attack and defense addressed in different graph analysis tasks, leading to the arms race in graph adversarial learning. Despite the booming works, there still lacks a unified problem definition and a comprehensive review. To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically. Specifically, we survey and unify the existing works w.r.t. attack and defense in graph analysis tasks, and give appropriate definitions and taxonomies at the same time. Besides, we emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively. Hopefully, our works can provide a comprehensive overview and offer insights for the relevant researchers. Latest advances in graph adversarial learning are summarized in our GitHub repository https://github.com/EdisonLeeeee/Graph-Adversarial-Learning.
