Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning-Reference-Cited by-同舟云学术

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Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning

Published:2024-07-10 Issue: Volume:42 Page:1-12
ISSN:
Container-title:Proceedings of the 36th International Conference on Scientific and Statistical Database Management
language:
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Author:

Wang Zitong¹^ORCID,Luo Xuexiong²^ORCID,Song Enfeng³^ORCID,Bai Qiuqing⁴^ORCID,Lin Fu⁵^ORCID

Affiliation:

1. School of Cyber Science and Engineering, Wuhan University, China

2. The School of Computing, School of Computing, Macquarie University, Australia

3. Renmin Hospital of Wuhan University, China

4. School of Computer Science, Wuhan University, China

5. School of Computer Science, Wuhan University, China and School of Cyber Science and Engineering, Wuhan University, China

Funder

Renmin Hospital of Wuhan University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3676288.3676292

Reference48 articles.

1. Markus M Breunig Hans-Peter Kriegel Raymond T Ng and Jörg Sander. 2000. LOF: identifying density-based local outliers. In SIGMOD. 93–104.

2. Ruth MJ Byrne. 2019. Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning.. In IJCAI. 6276–6282.

3. Anshika Chaudhary Himangi Mittal and Anuja Arora. 2019. Anomaly Detection using Graph Neural Networks. In COMITCon. 346–350. https://doi.org/10.1109/COMITCon.2019.8862186

4. Counterfactual Samples Synthesizing and Training for Robust Visual Question Answering

5. Braingb: a benchmark for brain network analysis with graph neural networks;Cui Hejie;IEEE Transactions on Medical Imaging,2022

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