Error-Bounded Graph Anomaly Loss for GNNs-Reference-Cited by-同舟云学术

Error-Bounded Graph Anomaly Loss for GNNs

Published:2020-10-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM International Conference on Information & Knowledge Management
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Author:

Zhao Tong¹,Deng Chuchen¹,Yu Kaifeng¹,Jiang Tianwen²,Wang Daheng¹,Jiang Meng¹

Affiliation:

1. University of Notre Dame, Notre Dame, IN, USA

2. University of Notre Dame & Harbin Institute of Technology, Harbin, China

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3340531.3411979

Reference53 articles.

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2. Leman Akoglu Hanghang Tong and Danai Koutra. 2015. Graph based anomaly detection and description: a survey. Data mining and knowledge discovery Vol. 29 3 (2015) 626--688. Leman Akoglu Hanghang Tong and Danai Koutra. 2015. Graph based anomaly detection and description: a survey. Data mining and knowledge discovery Vol. 29 3 (2015) 626--688.

3. A local algorithm for finding dense subgraphs;Andersen Reid;ACM Transactions on Algorithms (TALG),2010

4. Greedily Finding a Dense Subgraph

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