Multi-Scale GCAD and GNN-Based Anomaly Detection Latency Across Graph Sizes

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: What is the inference latency scaling behavior of multi-scale GCAD models compared to non-contrastive GNN-based anomaly detection methods on graphs with varying sizes (e.g., Cora vs. Reddit), and how. As industries become automated and connectivity technologies advance, a wide range of systems continues to generate massive amounts of data. Many approaches have been proposed to extract principal indicators from the vast sea of data to represent the entire system state. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the inference latency scaling behavior of multi-scale GCAD models compared to non-contrastive GNN-based anomaly detection methods on graphs with varying sizes (e.g., Cora vs. Reddit), and how does this impact real-time deployment feasibility?

Autonomous literature synthesis. Automated review score: 8.0/10. Full text and citation available at Assignee Research.