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.

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:

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.