Graph Anomaly Detection with Few Labels: A Data-Centric Approach-Reference-Cited by-同舟云学术

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Graph Anomaly Detection with Few Labels: A Data-Centric Approach

Published:2024-08-24 Issue: Volume:30 Page:2153-2164
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Ma Xiaoxiao¹^ORCID,Li Ruikun²^ORCID,Liu Fanzhen¹^ORCID,Ding Kaize³^ORCID,Yang Jian¹^ORCID,Wu Jia¹^ORCID

Affiliation:

1. School of Computing, Macquarie University, Sydney, New South Wales, Australia

2. Business School, The University of Sydney, Sydney, New South Wales, Australia

3. Department of Statistics and Data Science, Northwestern University, Evanston, Illinois, USA

Funder

Macquarie University

Australian Research Councile

Australian Research Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671929

Reference56 articles.

1. Leman Akoglu. 2021. Anomaly mining: Past present and future. In CIKM. 1--2.

2. Graph based anomaly detection and description: a survey

3. Sambaran Bandyopadhyay, Lokesh N, Saley Vishal Vivek, and M. N. Murty. 2020. Outlier resistant unsupervised deep architectures for attributed network embedding. In WSDM. 25--33.

4. Ziwei Chai Siqi You Yang Yang Shiliang Pu Jiarong Xu Haoyang Cai and Weihao Jiang. 2022. Can abnormality be detected by graph neural networks?. In IJCAI. 1945--1951.

5. Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat GANs on image synthesis. In NeurIPS. 8780--8794.

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