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README.md

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+---
+tags: [graph-neural-network, cybersecurity, rgcn, pytorch-geometric, multi-cloud]
+---
+# Stage 6 — Heterogeneous Structural GNN (RGCN)
+## Multi-Cloud Threat Detection Pipeline — Holistic Version
+
+**Library**: PyTorch Geometric (RGCNConv)
+**Design**: Schema-agnostic — works with any node/edge types, any feature dimension
+
+### Architecture
+- Input: any fdim → zero-padded to MAX_FDIM=1024 → Linear(1024→256)
+- 3-layer RGCNConv (256→256→128), num_relations=20
+- PEFT Adapters (rank=16) after layers 1 and 2
+- DistMult edge anomaly scoring per relation type
+
+### Master Schema
+- **Node types (15)**: User, VM, IP, Role, CVE, Container, CloudAccount, Subnet, Bucket, Function, Cluster, Pod, Database, LoadBalancer, Gateway
+- **Edge types (20)**: EXPLOITS, CROSS_CLOUD_ACCESS, CONNECTS_TO, EXPLOITS, ACCESS, ASSUMES_ROLE, CONNECTS_TO, RESTART_VM, START_VM, STOP_VM, DEPLOYED_ON, HAS_VULN, LATERAL_MOVEMENT, GRANTS_ACCESS, RUNS_ON, EXPLOITS, ACCESS, TRIGGERS, ACCESS, CONTAINS
+
+### Active Schema (this run)
+- **Node types**: User, VM, IP, Role, CVE
+- **Edge types**: 10
+- **Supervised**: User, Role
+
+## Ablation Results (Test Set)
+
+| Model   | Type     | Params    |   User_AUC |   User_F1 |   User_AP |   Role_AUC |   Role_F1 |   Role_AP |
+|:--------|:---------|:----------|-----------:|----------:|----------:|-----------:|----------:|----------:|
+| RGCN    | PRIMARY  | 8,515,599 |        0.5 |         0 |       0.5 |        0.5 |         0 |       0.5 |
+| GCN     | BASELINE | 5,219,855 |        0.5 |         0 |       0.5 |        0.5 |         0 |       0.5 |
+| GAT     | BASELINE | 5,320,207 |        0.5 |         0 |       0.5 |        0.5 |         0 |       0.5 |
+| SAGE    | BASELINE | 5,383,695 |        0.5 |         0 |       0.5 |        0.5 |         0 |       0.5 |
+
+## Usage — Stage 7 API Integration
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# Load once, call forever
+ckpt  = torch.load(hf_hub_download("adarsh-aur/rgcn-security-zero-embedding", "model_RGCN.pt"))
+model = HeteroRGCN()
+model.load_state_dict(ckpt['model_state_dict'])
+model.eval()
+
+# Works with any graph snapshot from Stage 5
+with torch.no_grad():
+    h_v, offsets, logits = model(graph_snapshot)
+    # h_v shape: [total_nodes, 128]  → feed to Stage 7 GRU
+    # New node/edge types: silently skipped
+    # Missing node/edge types: silently skipped
+    # Different fdim: auto-padded/truncated to 1024
+```