Outlier detection variational autoencoder

Anomaly detection in graph-based data is an emerging field in machine learning with many relevant applications. Although some algorithms have been developed, current models lack consistency on real-world data and often have problems with overfitting. The paper presents a new model to address these c...

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Bibliographic Details
Published in:	Neural computing & applications Vol. 37; no. 21; pp. 16871 - 16882
Main Authors:	Powers, Henry, Edoh, Kossi
Format:	Journal Article
Language:	English
Published:	London Springer London 01.07.2025 Springer Nature B.V
Subjects:	Accuracy Algorithms Anomalies Artificial Intelligence Cognitive tasks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Convolution Data analysis Data Mining and Knowledge Discovery Datasets Deep learning Graph neural networks Graphs Image Processing and Computer Vision Machine learning Neural networks Original Article Outliers (statistics) Probability and Statistics in Computer Science Social networks Variational autoencoder Graph neural network Anomaly
ISSN:	0941-0643, 1433-3058
Online Access:	Get full text
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