计算机科学
人工智能
异常检测
对抗制
生成语法
生成对抗网络
机器学习
深度学习
模式识别(心理学)
异常(物理)
鉴别器
作者
Zhenxing Chen,Bo Liu,Meiqing Wang,Peng Dai,Jun Lv,Liefeng Bo
出处
期刊:Conference on Information and Knowledge Management
日期:2020-10-19
卷期号:: 1989-1992
被引量:3
标识
DOI:10.1145/3340531.3412070
摘要
Anomaly detection is a useful technique in many applications such as network security and fraud detection. Due to the insufficiency of anomaly samples as training data, it is usually formulated as an unsupervised model learning problem. In recent years there is a surge of adopting graph data structure in numerous applications. Detecting anomaly in an attributed network is more challenging than the sample based task because of the sample information representations in the form of graph nodes and edges. In this paper, we propose a generative adversarial attributed network (GAAN) anomaly detection framework. The fake graph nodes are generated by a generator module with Gaussian noise as input. An encoder module is employed to map both real and fake graph nodes into a latent space. To encode the graph structure information into the node latent representation, we compute the sample covariance matrix for real nodes and fake nodes respectively. A discriminator is trained to recognize whether two connected nodes are from the real or fake graph. With the learned encoder module output, an anomaly evaluation measurement considering the sample reconstruction error and real-sample identification confidence is employed to make prediction. We conduct extensive experiments on benchmark datasets and compare with state-of-the-art attributed graph anomaly detection methods. The superior AUC score demonstrates the effectiveness of the proposed method.
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