Quantization and Pruning Effects on GNN-Based Anomaly Detection in Edge Devices

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: What is the impact of quantization techniques on inference latency and F1 score for GNN-based anomaly detection models deployed on resource-constrained devices, and how does it compare to model. Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is based on the social value of Generation Z that online and offline selves are not different. With the technological development of deep learning-based high-precision recognition models and. 6 claims were extracted from source literature; 6 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

What is the impact of quantization techniques on inference latency and F1 score for GNN-based anomaly detection models deployed on resource-constrained devices, and how does it compare to model pruning methods?

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