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README.md

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+---
+language: en
+license: mit
+tags:
+  - graph-neural-network
+  - rgcn
+  - heterogeneous-graph
+  - cybersecurity
+  - anomaly-detection
+  - pytorch-geometric
+  - multi-cloud
+  - threat-detection
+pipeline_tag: graph-ml
+---
+
+# Stage 6: Heterogeneous RGCN — Multi-Cloud Threat Detection
+
+Part of the research project:
+**Intelligent Threat Detection using GNN in Multi-Cloud Environments**
+
+## Architecture
+
+| Property | Value |
+|---|---|
+| Model | Heterogeneous RGCN (RGCNConv) |
+| Framework | PyTorch Geometric |
+| Init Strategy | GraphSAGE-style projection (Stanford) |
+| PEFT | GNN Adapter layers (residual bottleneck) |
+| Hidden Dim | 256 |
+| Output Dim | 128 |
+| Layers | 3 |
+| Node Types | user, device, cve, cloud, role, ip |
+| Edge Types | assumes_role, accesses, connects_to, has_vuln, deployed_on, lateral_movement, exploits, grants_access |
+| Task | Node threat scoring + edge anomaly detection |
+| Best Val AUC | 0.9853 |
+
+## Graph Schema
+
+```
+user ──assumes_role──► role ──grants_access──► cloud
+user ──accesses──────► device ──has_vuln──────► cve
+user ──connects_to───► ip ──connects_to───────► device
+device ──lateral_movement──► device
+device ──deployed_on──► cloud
+cve ──exploits──────────────► device
+```
+
+## Sample Output
+
+```
+Node:        user_4456
+Type:        user
+Probability: 0.7231
+Risk:        🔴 HIGH
+Top Edge:    lateral_movement
+
+Influence per relation:
+  lateral_movement    +0.21031
+  has_vuln            +0.14203
+  assumes_role        +0.08821
+  accesses            −0.03102
+```
+
+## How to Load
+
+```python
+import torch, json
+from huggingface_hub import hf_hub_download
+
+weights_path = hf_hub_download(repo_id="adarsh-aur/Heterogenous_GNN", filename="model_final.pt")
+config_path  = hf_hub_download(repo_id="adarsh-aur/Heterogenous_GNN", filename="config.json")
+
+with open(config_path) as f:
+    cfg = json.load(f)
+
+model = HeteroRGCN(
+    feat_dims     = cfg["feat_dims"],
+    node_types    = cfg["node_types"],
+    edge_types    = [tuple(et) for et in cfg["edge_types"]],
+    hidden_dim    = cfg["hidden_dim"],
+    out_dim       = cfg["out_dim"],
+    num_layers    = cfg["num_layers"],
+    dropout       = cfg["dropout"],
+    adapter_rank  = cfg["adapter_rank"],
+    predict_types = cfg["predict_types"],
+)
+model.load_state_dict(torch.load(weights_path, map_location="cpu"))
+model.eval()
+```
+
+## Pipeline Position
+
+| Stage | Component |
+|---|---|
+| Stage 5 | Graph Construction + Feature Fusion |
+| **Stage 6** | **Heterogeneous Structural GNN ← this model** |
+| Stage 7 | Temporal GNN |
+| Stage 8 | Risk Fusion MLP |
+
+## Training Details
+
+| Property | Value |
+|---|---|
+| Optimizer | AdamW |
+| Scheduler | CosineAnnealingWarmRestarts |
+| Loss | BCEWithLogitsLoss + pos_weight (imbalance correction) |
+| Epochs | 150 |
+| Hardware | Kaggle T4 x2 |