Train-Test Split Protocols and Metric Robustness in Graph Anomaly Detection

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does the choice of train-test split protocol in graph anomaly detection models affect the robustness of the F1 score and AUC metrics across different graph densities. Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. 10 claims were extracted from source literature; 10 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 Question

How does the choice of train-test split protocol in graph anomaly detection models affect the robustness of the F1 score and AUC metrics across different graph densities?

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