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adarsh-aur
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Heterogenous_GNN

Graph Machine Learning
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graph-neural-network
rgcn
heterogeneous-graph
cybersecurity
anomaly-detection
pytorch-geometric
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Heterogenous_GNN
File size: 1,377 Bytes
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{
  "hidden_dim": 256,
  "out_dim": 128,
  "num_layers": 3,
  "dropout": 0.3,
  "lr": 0.001,
  "weight_decay": 0.0005,
  "epochs": 150,
  "adapter_rank": 16,
  "threat_ratio": 0.15,
  "threshold": 0.5,
  "model": "HeteroRGCN",
  "framework": "PyTorch Geometric",
  "init_strategy": "GraphSAGE-style projection (Stanford)",
  "peft": {
  "type": "gnn_adapter",
  "architecture": "residual_bottleneck",
  "adapter_rank": 16
  },
  "node_types": [
    "user",
    "device",
    "cve",
    "cloud",
    "role",
    "ip"
  ],
  "edge_types": [
    [
      "user",
      "assumes_role",
      "role"
    ],
    [
      "user",
      "accesses",
      "device"
    ],
    [
      "user",
      "connects_to",
      "ip"
    ],
    [
      "device",
      "has_vuln",
      "cve"
    ],
    [
      "device",
      "deployed_on",
      "cloud"
    ],
    [
      "device",
      "lateral_movement",
      "device"
    ],
    [
      "cve",
      "exploits",
      "device"
    ],
    [
      "role",
      "grants_access",
      "cloud"
    ],
    [
      "ip",
      "connects_to",
      "device"
    ]
  ],
  "feat_dims": {
    "user": 320,
    "device": 386,
    "cve": 130,
    "cloud": 320,
    "role": 128,
    "ip": 128
  },
  "predict_types": [
    "user",
    "device"
  ],
  "best_epoch": 112,
  "best_val_auc": 0.985262824936738,
  "hf_repo": "adarsh-aur/Heterogenous_GNN"
}