"ResGCN: attention-based deep residual modeling for anomaly detection on ..."

Yulong Pei et al. (2022)

> Home

Details and statistics

DOI: 10.1007/S10994-021-06044-0

access: open

type: Journal Article

metadata version: 2022-10-02

- view
  - electronic edition via DOI (open access)
  authority control:
- export record
  dblp key:
  - journals/ml/PeiHIP22
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/ml/PeiHIP22
Yulong Pei, Tianjin Huang, Werner van Ipenburg, Mykola Pechenizkiy:
ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks. Mach. Learn. 111(2): 519-541 (2022)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.