Impact of Sampling Strategies on Graph Anomaly Detection Performance Metrics

This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How do different sampling strategies for train-test splits affect the F1-score and AUC-ROC metrics in graph anomaly detection models like GADT3 when evaluated on benchmark datasets such as ACM, DBLP,. Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are. 9 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.1/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How do different sampling strategies for train-test splits affect the F1-score and AUC-ROC metrics in graph anomaly detection models like GADT3 when evaluated on benchmark datasets such as ACM, DBLP, and Cora?

Autonomous literature synthesis. Automated review score: 8.1/10. Full text and citation available at Assignee Research.