---
license: apache-2.0
task_categories:
- tabular-classification
tags:
- graph-neural-network
- cybersecurity
- node-classification
- pytorch
---

# MeshGraph GCN

MeshGraph is a graph convolutional node classifier for detecting compromised assets
inside a simulated enterprise network. Nodes carry host telemetry, while edges encode
observed communication. Compromise begins at sparse seeds and propagates stochastically
through the network.

The GCN is compared with a logistic-regression baseline that sees identical host
features but cannot use graph structure.

This is a transductive benchmark: the graph and all node features are visible during
training, while validation and test labels remain hidden.

## Reproduce

```powershell
uv run python projects/meshgraph-gcn/generate_data.py
uv run python projects/meshgraph-gcn/train.py
```

## Verified results

The generated graph contains 600 nodes, 1,689 undirected communication edges, six
subnets, and a 23.33% compromise rate. The final test contains 120 nodes:

| Model | Parameters | Accuracy | ROC-AUC | Average precision | F1 |
| --- | ---: | ---: | ---: | ---: | ---: |
| Feature-only logistic regression | 9 fitted coefficients | 78.33% | 0.8362 | 0.6274 | 0.5938 |
| Two-layer GCN | 802 | **81.67%** | **0.8564** | **0.6976** | **0.6333** |

Both thresholds were selected independently on the same 120-node validation split.
The GCN improved F1 by 3.96 points and average precision by 7.02 points.