Spectral and Spatial Graph Anomaly Detection Robustness Under Feature Masking on Heterophilic Graphs

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How do spectral-based graph anomaly detection methods compare to spatial GNN baselines in robustness when 20\% of node features are masked on heterophilic graphs. 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. 17 claims were extracted from source literature; 2 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How do spectral-based graph anomaly detection methods compare to spatial GNN baselines in robustness when 20% of node features are masked on heterophilic graphs?

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