GNN Architecture Trade-offs in Mul-GAD for Large-Scale Graph Anomaly Detection

Abstract

Abstract: This report synthesises findings from 8 peer-reviewed papers addressing the following research question: How do different GNN architectures (e.g., GraphSAGE vs. GAT) compare in terms of inference latency and memory efficiency when deployed in the Mul-GAD framework for large-scale graph anomaly detection. Anomaly detection is defined as discovering patterns that do not conform to the expected behavior. Previously, anomaly detection was mostly conducted using traditional shallow learning techniques, but with little improvement. 18 claims were extracted from source literature; 3 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How do different GNN architectures (e.g., GraphSAGE vs. GAT) compare in terms of inference latency and memory efficiency when deployed in the Mul-GAD framework for large-scale graph anomaly detection?

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