BFS_EA_RGCN Model

This is a Graph Neural Network model combining Residual Graph Convolutional Networks (RGCN) and Edge Attention mechanisms, designed for binary classification of graphs (vulnerable vs. non-vulnerable).

Architecture: Classifier with res_GCN (9 layers, 11 hidden units) and Edge_Attention (2 heads, 100 feature length)
Task: Binary classification of software graphs
Dataset: SG_Final_Train and SG_Final_Test (vulnerable and non-vulnerable graphs)
Input: Graph node features (100-dim), edge features (100-dim), adjacency matrix
Output: Binary label (0: non-vulnerable, 1: vulnerable)
Checkpoint: Loaded from /kaggle/input/egat-peculiar-model/checkpoint_epoch_4.pt
Performance:
- Test Accuracy: ~0.5959
- Precision: ~0.6345
- Recall: ~0.4623
- F1 Score: ~0.5349
- AUC: ~0.6222 (from exp-0 in notebook)

For more details, refer to the original training notebook or dataset documentation.