Multi-View Aggregation in Graph Anomaly Detection: Scalability, F1 Scores, and Latency Trade-offs

Abstract

Abstract: This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does multi-view aggregation in graph anomaly detection frameworks affect the F1 score and inference latency when scaling from single-edge devices to distributed edge computing environments. Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are. 10 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does multi-view aggregation in graph anomaly detection frameworks affect the F1 score and inference latency when scaling from single-edge devices to distributed edge computing environments?

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