Semi-Supervised vs. Unsupervised GNN Anomaly Detectors in Temporal Graph Benchmarks

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does the inference throughput of semi-supervised GNN anomaly detectors like Mul-GAD compare to unsupervised methods (e.g., DOMINANT) when evaluated on temporal graph benchmarks with node sizes. Graph neural networks (GNNs) have recently garnered significant attention for use in network intrusion detection systems (NIDS), owing to their ability to model network traffic as graphs and capture complex dependencies between flows. However, existing GNN-based methods face. 17 claims were extracted from source literature; 14 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the inference throughput of semi-supervised GNN anomaly detectors like Mul-GAD compare to unsupervised methods (e.g., DOMINANT) when evaluated on temporal graph benchmarks with node sizes ranging from 10K to 100K, measured in nodes per second (NPS)?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance