Coding of Graphs with Application to Graph Anomaly Detection
Coding of Graphs with Application to Graph Anomaly Detection
复制标题
图编码及其在图异常检测中的应用
DOI：
10.1109/isit.2018.8437551
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
June Zhang
中科院分区：
文献类型：
--
作者：
A. Høst;June Zhang
本文具有双重目标。首先是为未标记图开发实用的通用编码方法。第二是将它们用于图形异常检测。本文为未标记的图开发了两种编码方法：一个基于度分布，第二个基于三角形分布。结果表明，这些对于不同类型的随机图和现实图表有效。然后，这些编码方法仅基于结构来检测异常图。结果表明，可以很高的概率检测到异常图。
This paper has dual aims. First is to develop practical universal coding methods for unlabeled graphs. Second is to use these for graph anomaly detection. The paper develops two coding methods for unlabeled graphs: one based on the degree distribution, the second based on the triangle distribution. It is shown that these are efficient for different types of random graphs, and on real-world graphs. These coding methods is then used for detecting anomalous graphs, based on structure alone. It is shown that anomalous graphs can be detected with high probability.