Semi-Supervised vs. Supervised Graph Anomaly Detection at Scale: Efficiency Trade-Offs

This report synthesises findings from 8 peer-reviewed papers addressing the following research question: What is the computational efficiency trade-off between semi-supervised graph anomaly detection frameworks and fully supervised methods when scaled to large-scale graphs, measured by inference latency. Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the computational efficiency trade-off between semi-supervised graph anomaly detection frameworks and fully supervised methods when scaled to large-scale graphs, measured by inference latency and memory usage?

Autonomous literature synthesis. Automated review score: 9.2/10. Full text and citation available at Assignee Research.