| title: MeshGraph GAT | |
| emoji: 🕸️ | |
| colorFrom: red | |
| colorTo: violet | |
| sdk: gradio | |
| sdk_version: "6.5.1" | |
| app_file: app.py | |
| pinned: false | |
| # MeshGraph GAT | |
| 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. | |
| ## Verified local result | |
| 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. | |
| ```bash | |
| uv run python projects/meshgraph-gat/train.py | |
| uv run pytest tests/test_meshgraph_gat.py | |
| ``` | |