Multi-View vs. Single-View Graph Anomaly Detection: Efficiency and Scalability on Large-Scale Sparse Graphs

This report synthesises findings from 5 peer-reviewed papers addressing the following research question: How does the inference efficiency and scalability of multi-view graph anomaly detection frameworks compare to single-view baselines when processing large-scale sparse graphs, measured in samples per. Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections. 11 claims were extracted from source literature; 9 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: How does the inference efficiency and scalability of multi-view graph anomaly detection frameworks compare to single-view baselines when processing large-scale sparse graphs, measured in samples per second and memory usage?

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