MeshGraph GAT applies four-head graph attention to the same 600-node enterprise compromise benchmark used by MeshGraph GCN. Validation and test labels remain held out in the transductive graph.

The report compares GAT with the previously trained GCN and feature-only logistic control, then audits normalized attention entropy and the highest-weight test edges. The Space exposes neighbor attention for every node.

The 772-parameter GAT reached 0.8412 ROC-AUC, 0.6316 average precision, and 0.5965 F1. The 802-parameter GCN remained stronger at 0.8564/0.6976/0.6333. Mean normalized GAT attention entropy was 0.929, indicating broadly distributed rather than sharply selective neighbor weighting.