论文标题
大型标记的多毛图数据库中的异常检测
Anomaly Detection in Large Labeled Multi-Graph Databases
论文作者
论文摘要
在包含标有标记节点和定向的多边缘的图形的大数据库G中；我们如何检测异常图？大多数现有的工作都是为普通（未标记）和/或简单（未加权）图的设计的。我们介绍CodeTect，这是第一种解决具有如此复杂性质的图形数据库的异常检测任务的方法。为此，它可以尽可能地识别出结构模式（即节点标记的网络主题）的小型代表性集合，该集合会尽可能地无效地压缩数据库G。压缩良好的图形被标记为异常。 CODETECT exhibits two novel building blocks: (i) a motif-based lossless graph encoding scheme, and (ii) fast memory-efficient search algorithms for S. We show the effectiveness of CODETECT on transaction graph databases from three different corporations, where existing baselines adjusted for the task fall behind significantly, across different types of anomalies and performance metrics.
Within a large database G containing graphs with labeled nodes and directed, multi-edges; how can we detect the anomalous graphs? Most existing work are designed for plain (unlabeled) and/or simple (unweighted) graphs. We introduce CODETECT, the first approach that addresses the anomaly detection task for graph databases with such complex nature. To this end, it identifies a small representative set S of structural patterns (i.e., node-labeled network motifs) that losslessly compress database G as concisely as possible. Graphs that do not compress well are flagged as anomalous. CODETECT exhibits two novel building blocks: (i) a motif-based lossless graph encoding scheme, and (ii) fast memory-efficient search algorithms for S. We show the effectiveness of CODETECT on transaction graph databases from three different corporations, where existing baselines adjusted for the task fall behind significantly, across different types of anomalies and performance metrics.
