GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction-Reference-Cited by-同舟云学术

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GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction

Published:2024-03-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
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Author:

Roy Amit¹^ORCID,Shu Juan¹^ORCID,Li Jia²^ORCID,Yang Carl³^ORCID,Elshocht Olivier⁴^ORCID,Smeets Jeroen⁴^ORCID,Li Pan⁵^ORCID

Affiliation:

1. Purdue University, West Lafayette, USA

2. Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

3. Emory University, Atlanta, USA

4. Sony R&D Center Brussels Laboratory, Zaventem, Belgium

5. Georgia Institute of Technology, Atlanta, USA

Funder

NSF (National Science Foundation) grant

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635767

Reference83 articles.

1. Outlier detection for high dimensional data

2. oddball: Spotting Anomalies in Weighted Graphs

3. Graph based anomaly detection and description: a survey

4. Fast and reliable anomaly detection in categorical data

5. Outlier Aware Network Embedding for Attributed Networks

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DeGAN - Decomposition-based unified anomaly detection in static networks;Information Sciences;2024-08

2. DBAD: Dual branch reconstruction for industrial anomaly detection;Electronics Letters;2024-08

3. Simultaneously Detecting Node and Edge Level Anomalies on Heterogeneous Attributed Graphs;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

4. Detecting Anomalies in Attributed Networks Through Sparse Canonical Correlation Analysis Combined With Random Masking and Padding;IEEE Access;2024

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