Multi-View vs. Single-View Robustness in Semi-Supervised Graph Anomaly Detection on OGBN-Proteins

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the robustness of semi-supervised graph anomaly detection models compare when using multi-view aggregation versus single-view representations on the OGBN-Proteins benchmark across different. Graph Convolutional Network (GCN) has achieved significant success in many graph representation learning tasks. GCN usually learns graph representations by performing Neighbor Aggregation (NA) and Feature Transformation (FT) operations. 8 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.9/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the robustness of semi-supervised graph anomaly detection models compare when using multi-view aggregation versus single-view representations on the OGBN-Proteins benchmark across different GNN architectures like GCN, GraphSAGE, and GAT?

Autonomous literature synthesis. Automated review score: 7.9/10. Full text and citation available at Assignee Research.