Graph iForest: Isolation of anomalous and outlier graphs-Reference-Cited by-同舟云学术

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Graph iForest: Isolation of anomalous and outlier graphs

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 International Joint Conference on Neural Networks (IJCNN)
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Author:

Zambon Daniele¹,Livi Lorenzo²,Alippi Cesare³

Affiliation:

1. Universita della Svizzera italiana,The Swiss AI Lab IDSIA,Lugano,Switzerland

2. University of Manitoba Winnipeg, Manitoba, Canada University of Exeter,Exeter,United Kingdom

3. The Swiss AI Lab IDSIA Universita della Svizzera italiana Lugano, Switzerland Politecnico di Milano,Milano,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9891857/9889787/09892295.pdf?arnumber=9892295

Reference27 articles.

1. Detecting sequences of system states in temporal networks

2. A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

3. A review of novelty detection

4. Isolation Forest

Cited by 2 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. Anomaly Detection with Generalized Isolation Forest;Lecture Notes on Data Engineering and Communications Technologies;2024

2. Asymptotic Consistent Graph Structure Learning for Multivariate Time-Series Anomaly Detection;IEEE Transactions on Instrumentation and Measurement;2024

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