论文标题
归因网络中用于异常检测的一级图神经网络
One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks
论文作者
论文摘要
如今，图形结构化数据越来越多地用于建模复杂系统。同时，从图形中检测异常已成为紧迫社会问题的重要研究问题。异常检测是一项无监督的学习任务，识别与大多数不同的稀有数据。作为主要的异常检测算法之一，一类支持向量机已被广泛用于检测异常值。但是，这些传统的异常检测方法在图形数据中失去了有效性。由于传统的异常检测方法稳定，稳定且易于使用，因此将它们推广到图形数据至关重要。在这项工作中，我们提出了一个类图形神经网络（OCGNN），这是图形异常检测的一类分类框架。 OCGNN旨在将图形神经网络的强大表示能力与经典的一级目标结合在一起。与其他基线相比，OCGNN在广泛的实验中取得了重大改进。
Nowadays, graph-structured data are increasingly used to model complex systems. Meanwhile, detecting anomalies from graph has become a vital research problem of pressing societal concerns. Anomaly detection is an unsupervised learning task of identifying rare data that differ from the majority. As one of the dominant anomaly detection algorithms, One Class Support Vector Machine has been widely used to detect outliers. However, those traditional anomaly detection methods lost their effectiveness in graph data. Since traditional anomaly detection methods are stable, robust and easy to use, it is vitally important to generalize them to graph data. In this work, we propose One Class Graph Neural Network (OCGNN), a one-class classification framework for graph anomaly detection. OCGNN is designed to combine the powerful representation ability of Graph Neural Networks along with the classical one-class objective. Compared with other baselines, OCGNN achieves significant improvements in extensive experiments.
