Deep Anomaly Detection on Attributed Networks

自编码 计算机科学 异常检测 深度学习 人工智能 节点(物理) 子空间拓扑 数据挖掘 机器学习 工程类 结构工程
作者
Kaize Ding,Jundong Li,Rohit Bhanushali,Liu Huan
出处
期刊:Society for Industrial and Applied Mathematics eBooks [Society for Industrial and Applied Mathematics]
卷期号:: 594-602 被引量:214
标识
DOI:10.1137/1.9781611975673.67
摘要

Attributed networks are ubiquitous and form a critical component of modern information infrastructure, where additional node attributes complement the raw network structure in knowledge discovery. Recently, detecting anomalous nodes on attributed networks has attracted an increasing amount of research attention, with broad applications in various high-impact domains, such as cybersecurity, finance, and healthcare. Most of the existing attempts, however, tackle the problem with shallow learning mechanisms by ego-network or community analysis, or through subspace selection. Undoubtedly, these models cannot fully address the computational challenges on attributed networks. For example, they often suffer from the network sparsity and data nonlinearity issues, and fail to capture the complex interactions between different information modalities, thus negatively impact the performance of anomaly detection. To tackle the aforementioned problems, in this paper, we study the anomaly detection problem on attributed networks by developing a novel deep model. In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN); and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data. The synergy between GCN and autoencoder enables us to spot anomalies by measuring the reconstruction errors of nodes from both the structure and the attribute perspectives. Extensive experiments on real-world attributed network datasets demonstrate the efficacy of our proposed algorithm.MSC codesKeywords:Anomaly DetectionAttributed NetworksGraph Convolutional NetworkDeep Autoencoder
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