摘要:近年来，随着Web 2.0的普及，使用图挖掘技术进行异常检测受到人们越来越多的关注.图异常检测在欺诈检测、入侵检测、虚假投票、僵尸粉丝分析等领域发挥着重要作用.在广泛调研国内外大量文献以及最新科研成果的基础上，按照数据表示形式将面向图的异常检测划分成静态图上的异常检测与动态图上的异常检测两大类，进一步按照异常类型将静态图上的异常分为孤立个体异常和群组异常检测两种类别，动态图上的异常分为孤立个体异常、群体异常以及事件异常这3种类型.对每一类异常检测方法当前的研究进展加以介绍，对每种异常检测算法的基本思想、优缺点进行分析、对比，总结面向图的异常检测的关键技术、常用框架、应用领域、常用数据集以及性能评估方法，并对未来可能的发展趋势进行展望.



Abstract:In recent years, with the popularization of Web 2.0, people pay more and more attentions to the graph anomaly detection. The graph anomaly detection plays an increasingly vital role in the field of fraud detection, intrusion detection, false voting, and zombie fan analysis. This paper presents a survey on existing approaches to address this problem and reviews the recent developed techniques to detect graph anomalies. The graph-oriented anomaly detection is divided into two types, the anomaly detection on static graph and the anomaly detection on dynamic graph. Existing work on static graph anomaly detection have identified two types of anomalies:One is individual anomaly that refers to the abnormal behaviors of individual entity, the other is group anomaly that occurs due to unusual patterns of groups. The anomaly on dynamic graph can be divided into three types:Isolated individual anomaly, group anomaly, and event anomaly. This paper introduces the current research progress of each kind of anomaly detection methods, and summarizes the key technologies, common frameworks, application fields, common data sets, and performance evaluation methods of graph-oriented anomaly detection. Finally, future research directions on graph anomaly detection are discussed.



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