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Anomalous Subgraph Detection in Given Expected Degree Networks with Deep Learning
2021
IEEE Access
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 anomalous behaviors of the networks, which result in detection performance degradation. To mitigate the limitations, in this paper, we first present an anomalous subgraph detection framework
doi:10.1109/access.2021.3073696
fatcat:hrj45ljmjzghjh7fyudgf76msu