论文标题
使用光谱图滤波的基于社区的异常检测
Community-based anomaly detection using spectral graph filtering
论文作者
论文摘要
几种应用程序具有社区结构，同一社区的节点具有相似的属性。网络中的异常检测是一个相关且广泛研究的研究主题，并在各个领域中进行了应用。尽管有大量的异常检测框架，但方法的文献缺乏，这些方法都考虑了归因图和网络的社区结构。本文提出了一种基于社区的异常检测算法，该算法使用基于光谱图的过滤器，该过滤器将网络群落结构包括在拉普拉斯矩阵中，该矩阵采用作为傅立叶变换的基础。此外，过滤器的截止频率的选择还考虑了发现的社区数量。在计算实验中，所提出的称为SPECF的策略在成功识别离散异常方面表现出了出色的表现。 SPECF比无视社区结构的基线要好，特别是对于社区重叠较高的网络而言。此外，我们提出了一项案例研究，以验证提出的方法，以研究巴西圣约瑟·多斯·坎波斯不同地区的Covid-19的传播。
Several applications have a community structure where the nodes of the same community share similar attributes. Anomaly or outlier detection in networks is a relevant and widely studied research topic with applications in various domains. Despite a significant amount of anomaly detection frameworks, there is a dearth on the literature of methods that consider both attributed graphs and the community structure of the networks. This paper proposes a community-based anomaly detection algorithm using a spectral graph-based filter that includes the network community structure into the Laplacian matrix adopted as the basis for the Fourier transform. In addition, the choice of the cutoff frequency of the filter considers the number of communities found. In computational experiments, the proposed strategy, called SpecF, showed an outstanding performance in successfully identifying even discrete anomalies. SpecF is better than a baseline disregarding the community structure, especially for networks with a higher community overlapping. Additionally, we present a case study to validate the proposed method to study the dissemination of COVID-19 in the different districts of São José dos Campos, Brazil.
