Anomalies represent rare observations (e.g., data records or events) that deviate significantly from others. Over several decades, the burst of information has attracted more attention on anomalies because of their significance in a wide range of disciplines Anomaly detection, which aims to identify rare observations, is among the most vital tasks in the world, and has shown its power in preventing detrimental events, such as financial fraud, network intrusion, and social spam. The detection task is typically solved by identifying outlying data points in the feature space and inherently overlooks the relational information in real-world data. Graphs have been prevalently used to represent the structural information, which raises the graph anomaly detection problem - identifying anomalous graph objects (i.e., nodes, edges and sub-graphs) in a single graph, or anomalous graphs in a database/set of graphs. However, conventional anomaly detection techniques cannot tackle this problem well because of the complexity of graph data. For the advent of deep learning, graph anomaly detection with deep learning has received a growing attention recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. We compile open-sourced implementations, public datasets, and commonly-used evaluation metrics to provide affluent resources for future studies. More importantly, we highlight twelve extensive future research directions according to our survey results covering unsolved and emerging research problems and real-world applications. With this survey, our goal is to create a "one-stop-shop" that provides a unified understanding of the problem categories and existing approaches, publicly available hands-on resources, and high-impact open challenges for graph anomaly detection using deep learning.
翻译:异常现象是罕见的观测(如数据记录或事件),这些观测与其他观测有明显不同。几十年来,信息暴发引起了对异常现象的更多关注,因为异常现象在广泛的学科中意义重大。 异常现象检测旨在识别罕见的观测,是全世界最重要的任务之一,表明它有能力防止有害事件,如金融欺诈、网络入侵和社会垃圾。 通常通过查明特征空间中的外围数据点来解决探测任务,并固有地忽略了真实世界数据中的关联信息。图表被普遍用来代表结构信息,这增加了图表异常现象检测应用问题――在单一图表中识别异常现象图表对象(如节点、边缘和子图),或数据库/图集中的异常现象图表。然而,常规异常现象检测技术无法很好地解决这一问题,因为图表数据的复杂性。随着深度调查的到来,图形异常现象检测方法最近日益受到关注。 在本次调查中,我们的目标是提供系统而全面的图表检测问题, 找出当前深度的异常现象调查对象(如节点、节点、边缘和子) 确定一个更深层次的图表目标,我们用来进行更深层次的深度的统计分析。