对抗制
异常检测
计算机科学
生成语法
图形
人工智能
深层神经网络
机器学习
深度学习
异常(物理)
理论计算机科学
凝聚态物理
物理
作者
Kaize Ding,Jundong Li,Nitin Agarwal,Huan Liu
标识
DOI:10.24963/ijcai.2020/179
摘要
Anomaly detection on attributed networks has attracted a surge of research attention due to its broad applications in various high-impact domains, such as security, finance, and healthcare. Nonetheless, most of the existing efforts do not naturally generalize to unseen nodes, leading to the fact that people have to retrain the detection model from scratch when dealing with newly observed data. In this study, we propose to tackle the problem of inductive anomaly detection on attributed networks with a novel unsupervised framework: Aegis (adversarial graph differentiation networks). Specifically, we design a new graph neural layer to learn anomaly-aware node representations and further employ generative adversarial learning to detect anomalies among new data. Extensive experiments on various attributed networks demonstrate the efficacy of the proposed approach.
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