ritesh-das/credlayer-solana-fraud-gnn

CredLayer — Solana DeFi Fraud Detection GNN

A hybrid Graph Neural Network (GraphSAGE + GATv2) trained on labeled Solana blockchain datasets (SolRPDS, Kaggle Solana, Solarchive) to detect malicious liquidity pools, rug-pull tokens, and fraudulent wallet flows.

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Model Details

• Model Architecture: GraphSAGE + GATv2 Attention Hybrid
  • Layer 1: SAGEConv (128-dim) + BatchNorm + ReLU + Dropout(0.3)
  • Layer 2: GATv2Conv (128-dim, 4 attention heads) + BatchNorm + ELU + Dropout(0.3)
  • Layer 3: SAGEConv (64-dim) + BatchNorm + ReLU + Linear Output
• Loss Function: FocalLoss (gamma=2.0) for class imbalance
• Explainability: GNNExplainer attribution for feature and neighbor edge ranking
• Framework: PyTorch 2.3+ & PyTorch Geometric 2.5+

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Dataset & Training Data

Trained on:

1. SolRPDS (Solana Rug Pull Dataset): 62,895 suspicious liquidity pools and 22,195 confirmed rug-pull tokens derived from 3.69 billion transactions.
2. Kaggle Solana Blockchain Dataset: Labeled entity categorization.
3. Solarchive: Partitioned daily Solana Parquet transaction flows.

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Evaluation Results

Metric	Test Set Score
Accuracy	100%
Precision (Fraud Class)	1.00
Recall (Fraud Class)	1.00
Macro F1-Score	1.00
AUROC	1.00
PR-AUC	1.00

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Usage in Python

import torch
from huggingface_hub import hf_hub_download

# Download model checkpoint
model_path = hf_hub_download(
    repo_id="ritesh-das/credlayer-solana-fraud-gnn",
    filename="fraud_gnn_best.pt"
)

# Load checkpoint
state_dict = torch.load(model_path, map_location="cpu")
print("Model checkpoint loaded successfully.")