论文标题
在动态图中的快速准确的异常检测，两种分配方法
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
论文作者
论文摘要
给定动态的图形流，我们如何检测异常模式的突然出现，例如链接垃圾邮件，追随者提升或拒绝服务攻击？此外，我们可以对实践中发生的异常类型进行分类，并理论上分析每种类型引起的异常符号？在这项工作中，我们提出了Anomrank，这是一种在线算法，用于动态图中的异常检测。 Anomrank采用了两种原始的方法，该方法定义了两个新颖的指标，以实现异常。每个度量标准跟踪其自己版本的“节点分数”（或节点重要性）函数的衍生物。这使我们能够检测到任何节点的重要性的突然变化。我们从理论上和实验上表明，两管沟的方法成功地检测了两种常见的异常类型：沿边缘的突然变化，以及图表的突然结构变化。 Anomrank是（a）快速准确：比最先进的方法快49.5倍，更准确35％，（b）可伸缩：在输入图中的边缘数中线性，在2秒内处理数百秒钟的股票笔记本电脑/桌面上的数百秒钟，并且（c）理论上的声音：提供了两种知识的方法。
Given a dynamic graph stream, how can we detect the sudden appearance of anomalous patterns, such as link spam, follower boosting, or denial of service attacks? Additionally, can we categorize the types of anomalies that occur in practice, and theoretically analyze the anomalous signs arising from each type? In this work, we propose AnomRank, an online algorithm for anomaly detection in dynamic graphs. AnomRank uses a two-pronged approach defining two novel metrics for anomalousness. Each metric tracks the derivatives of its own version of a 'node score' (or node importance) function. This allows us to detect sudden changes in the importance of any node. We show theoretically and experimentally that the two-pronged approach successfully detects two common types of anomalies: sudden weight changes along an edge, and sudden structural changes to the graph. AnomRank is (a) Fast and Accurate: up to 49.5x faster or 35% more accurate than state-of-the-art methods, (b) Scalable: linear in the number of edges in the input graph, processing millions of edges within 2 seconds on a stock laptop/desktop, and (c) Theoretically Sound: providing theoretical guarantees of the two-pronged approach.
