This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the semi-supervised multi-view aggregation approach in Mul-GAD compare to fully unsupervised GNN-based methods in terms of AUC performance on heterophilic graphs under adversarial edge. Benefiting from the message passing mechanism, Graph Neural Networks (GNNs) have been successful on flourish tasks over graph data. However, recent studies have shown that attackers can catastrophically degrade the performance of GNNs by maliciously modifying the graph structure. 9 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research.Research goal: How does the semi-supervised multi-view aggregation approach in Mul-GAD compare to fully unsupervised GNN-based methods in terms of AUC performance on heterophilic graphs under adversarial edge perturbations?Autonomous literature synthesis. Automated review score: 8.3/10. Full text and citation available at Assignee Research.