Graph Anomaly Detection Based on Steiner Connectivity and Density
Graph Anomaly Detection Based on Steiner Connectivity and Density
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DOI：
10.1109/jproc.2018.2813311
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发表时间：
2018-04
影响因子：
20.6
通讯作者：
Jose Cadena;F. Chen;A. Vullikanti
中科院分区：
文献类型：
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作者：
Jose Cadena;F. Chen;A. Vullikanti
检测“热点”和“异常”是一个反复出现的问题，具有广泛的应用，例如社交网络分析，流行病学，金融和生物监测等。网络是这些应用程序中用于表示复杂关系的常见抽象。通常，这些网络是动态的，即，它们会随着时间而演变。已经提出了许多方法用于在这样的动态网络数据集中进行异常检测，这些方法主要基于网络属性的变化。我们提供了一个调查的各种配方的异常检测动态网络的重点是“基于窗口”的方法。基于窗口的方法首先定义过去网络快照的时间窗口以模拟正常行为，然后如果快照具有与时间窗口中观察到的模式显著不同的模式，则将快照标记为异常。我们描述了两类技术：1）Steiner连通性的推广; 2）稠密子图挖掘。这两种方法都被广泛应用于基于窗口的图异常检测。我们总结了使用这些方法研究的关键问题配方，我们描述了一些主要技术的细节。
Detecting “hotspots” and “anomalies” is a recurring problem with a wide range of applications, such as social network analysis, epidemiology, finance, and biosurveillance, among others. Networks are a common abstraction in these applications for representing complex relationships. Typically, these networks are dynamic-, i.e., they evolve over time. A number of methods have been proposed for anomaly detection in such dynamic network data sets, which are primarily based on changes in network properties. We provide a survey of the various formulations of anomaly detection in dynamic networks with a focus on “window-based” methods. Window-based methods first define a time window of past network snapshots to model normal behavior and then mark a snapshot as anomalous if it has significantly different patterns from those observed in the time window. We describe two classes of techniques: 1) generalizations of Steiner connectivity; and 2) dense subgraph mining. Both have been used extensively in window-based graph anomaly detection. We summarize the key problem formulations that have been studied using these approaches, and we describe details of some of the main techniques.