Anomalous Subgraph Detection in Given Expected Degree Networks With Deep Learning

Anomalous subgraph detection within networks is an important issue in many emerging applications. Existing algorithms, such as graph structure methods and spectral feature methods, usually focus on the special stochastic model (such as the Erdős-Rényi random graph) or may not efficiently extract the...

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Veröffentlicht in:	IEEE access 2021, Vol.9, p.60052-60062
Hauptverfasser:	Luan, Mingan, Wang, Bo, Zhao, Yanping, Hu, Fengye
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Anomalous subgraph detection Anomaly detection Artificial neural networks convolutional neural network Deep learning Feature extraction given expected degree models Graph theory Neural networks Performance degradation Probability Signal to noise ratio Stochastic models Stochastic processes
Online-Zugang:	Volltext
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