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DAGAD: Data Augmentation for Graph Anomaly Detection

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Data Mining (ICDM)
language:
Short-container-title:

Author:

Liu Fanzhen¹,Ma Xiaoxiao¹,Wu Jia¹,Yang Jian¹,Xue Shan²,Beheshti Amin¹,Zhou Chuan³,Peng Hao⁴,Sheng Quan Z.¹,Aggarwal Charu C.⁵

Affiliation:

1. Macquarie University,School of Computing,Sydney,Australia

2. University of Wollongong,School of Computing and Information Technology,Wollongong,Australia

3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences,Beijing,China

4. Beihang University,Beijing Advanced Innovation Center for Big Data and Brain Computing,Beijing,China

5. IBM T. J. Watson Research Center,Yorktown,NY,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10027565/10027596/10027747.pdf?arnumber=10027747

Reference54 articles.

1. Reducing the Dimensionality of Data with Neural Networks

2. Deep learning for anomaly detection: A survey;chalapathy;arXiv preprint arXiv 1901 03912,2019

3. Few-shot Network Anomaly Detection via Cross-network Meta-learning

4. A Semi-Supervised Graph Attentive Network for Financial Fraud Detection

5. Pytorch: An imperative style, high-performance deep learning library;paszke;NeurIPS,2019

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2. Anomaly Detection for Telemetry Time Series Using a Denoising Diffusion Probabilistic Model;IEEE Sensors Journal;2024-05-15

3. Enhancing Intrusion Detection Through Data Perturbation Augmentation Strategy;2024 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT);2024-05-13

4. Decoupling Anomaly Discrimination and Representation Learning: Self-supervised Learning for Anomaly Detection on Attributed Graph;Data Science and Engineering;2024-05-04

5. Improving Graph Convolutional Network with Learnable Edge Weights and Edge-Node Co-Embedding for Graph Anomaly Detection;Sensors;2024-04-18

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