A Novel Graph-level Anomaly Detection Model

Author:

Wang Huawei1ORCID,Wang Huamin1ORCID,Guo Yijing1ORCID,Li Zhong2ORCID

Affiliation:

1. Xiamen University Tan Kah Kee College, China

2. College of Computer and Information Engineering, Xiamen University of Technology, China

Publisher

ACM

Reference22 articles.

1. Descriptive prediction of drug side‐effects using a hybrid deep learning model

2. Kipf, T.N., Welling, M. 2017. Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017) 1, 6,10

3. Zhang M. Chen Y. 2018. Link prediction based on graph neural networks. Advances in neural information processing systems 31 (2018) 1

4. Chong Zhou and Randy C Paffenroth. 2017. Anomaly detection with robust deep autoencoders. In KDD. 665–674.

5. Phuc Cuong Ngo, Amadeus Aristo Winarto, Connie Khor Li Kou, Sojeong Park, Farhan Akram, and Hwee Kuan Lee. 2019. Fence GAN: towards better anomaly detection. In ICTAI. IEEE, 141–148.

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