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meshgraph-gat
ARotting/meshgraph-gat
meshgraph-gat is a machine learning model from ARotting. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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.
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Updated Jul 30, 2026
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At a glance
- Access
- Public
- Created
- Jul 30, 2026
- Updated
- Jul 30, 2026
- SHA
- 60692807
Model card
From the Hugging Face model README
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.
uv run python projects/meshgraph-gat/train.py
uv run pytest tests/test_meshgraph_gat.py
- Created
- Jul 30, 2026
- Updated
- Jul 30, 2026