论文标题
用于异常分析的图形学习：算法，应用和挑战
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges
论文作者
论文摘要
在各种研究环境中，Anomaly Analytics是一项流行而至关重要的任务，已经研究了几十年。同时，深度学习显示了其在解决许多基于图的任务（例如节点分类，链接预测和图形分类）方面的能力。最近，许多研究正在扩展用于解决异常分析问题的图形学习模型，从而在基于图的异常分析技术方面取得了有益的进步。在这项调查中，我们提供了针对异常分析任务的图形学习方法的全面概述。我们根据它们的模型体系结构，即图形卷积网络（GCN），图形注意网络（GAT），Graph AutoCododer（GAE）和其他图形学习模型将它们分为四个类别。这些方法之间的差异也以系统的方式进行比较。此外，我们概述了现实世界中各个领域的几个基于图的异常分析应用程序。最后，我们讨论了这个快速增长的领域的五个潜在的未来研究方向。
Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks like, node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network (GCN), graph attention network (GAT), graph autoencoder (GAE), and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field.
