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近年来，随着web2.0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.本文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三种类型.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.
http://www.jos.org.cn/jos/ch/reader/view_abstract.aspx?file_no=6100&flag=1
面向图的异常检测研究综述
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Anomaly detection systems need to consider a lot of information when scanning for anomalies. One example is the context of the process in which an anomaly might occur, because anomalies for one process might not be anomalies for a different one. Therefore data -- such as system events -- need to be assigned to the program they originate from. This paper investigates whether it is possible to infer from a list of system events the program whose behavior caused the occurrence of these system events. To that end, we model transition probabilities between non-equivalent events and apply the $k$-nearest neighbors algorithm. This system is evaluated on non-malicious, real-world data using four different evaluation scores. Our results suggest that the approach proposed in this paper is capable of correctly inferring program names from system events.
