---
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
```