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| Anomaly Node Detection Method Based on Variational Graph Auto-Encoders in Attribute Networks |
| LI Zhong1,2, JIN Xiaolong1,2, WANG Yajie1,2, MENG Lingbin1,2, ZHUANG Chuanzhi1,2, SUN Zhi1,2 |
1. Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190;
2. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049 |
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Abstract Graph neural network provides a method of combining structural information and attribute information for attribute network data mining. However, the current unsupervised attribute network anomaly node detection based on graph auto-encoder cannot capture the sub-features of normal nodes, and the false negative rate is high.Anomaly node detection method based on variational graph auto-encoders is proposed to detect abnormal nodes in attribute networks, containing two encoders and a decoder. A variational auto-encoder model consisting of an encoder and a decoder is designed to reconstruct the original input data. A decoder and the second encoder are utilized to learn the latent representation of the network without abnormal node data. The features of normal nodes are learned by the dual variational auto-encoder, and the reconstruction error is applied as the anomaly measure of nodes. Normal nodes composed of normal node features are identified as normal nodes by taking reconstruction error as the anomaly measurement of nodes. Experiments on real network datasets show that the proposed method is able to detect abnormal nodes in attribute networks effectively.
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Received: 09 September 2021
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| Fund:National Key Research and Development Program of China(No.2016QY02D0404), National Natural Science Founda-tion of China(No.U1911401,61772501,U1836206,91646120) |
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Corresponding Authors:
LI Zhong, Ph.D. candidate. His research interests include network anomaly detection.
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About author:: JIN Xiaolong, Ph.D., professor. His research interests include knowledge graph and social network computing.
WANG Yajie, Ph.D. candidate. His research interests include information extraction.
MEMG Lingbin, Ph.D. candidate. His research interests include knowledge graph.
ZHUANG Chuanzhi,Ph.D. candidate. His research interests include relation extraction and natural language processing.
SUN Zhi,Ph.D. candidate. His research interests include knowledge graph and anomaly detection. |
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2022, Vol. 35 
