Full-Graph vs. Mini-Batch Training Robustness in Adversarial Graph Neural Networks

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: Does the choice between full-graph and mini-batch training pipelines affect the robustness of Graph Neural Networks against adversarial perturbations in control flow graphs used for security analysis. Malware remains a big threat to cyber security, calling for machine learning based malware detection. While promising, such detectors are known to be vulnerable to evasion attacks. 12 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 6.6/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

Does the choice between full-graph and mini-batch training pipelines affect the robustness of Graph Neural Networks against adversarial perturbations in control flow graphs used for security analysis?

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