HIGHLIGHTS
- who: Hedia Zardi and colleagues from the Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia have published the Article: Detecting Anomalies in Network Communities Based on Structural and Attribute Deviation, in the Journal: (JOURNAL) of 20/Nov/2022
- what: The authors propose a community-based anomaly detection approach called Community ANOMaly detection (CAnom). The authors focus on anomalous nodes that deviate from their community with respect to both the graph structure and node attributes. In contrast to traditional approaches that consider all the attributes, the authors focus on a subset of . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.