Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation-Reference-Cited by-同舟云学术

Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation

Published:2022-02-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
language:
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Author:

Ma Rongrong¹,Pang Guansong²,Chen Ling¹,van den Hengel Anton³

Affiliation:

1. University of Technology Sydney, Sydney, NSW, Australia

2. Singapore Management University, Singapore, Singapore

3. University of Adelaide, Adelaide, SA, Australia

Funder

ARC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3488560.3498473

Reference54 articles.

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2. Graph based anomaly detection and description: a survey

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

5. Yuri Burda , Harrison Edwards , Amos Storkey , and Oleg Klimov . 2018. Exploration by random network distillation. arXiv preprint arXiv:1810.12894 ( 2018 ). Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov. 2018. Exploration by random network distillation. arXiv preprint arXiv:1810.12894 (2018).

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