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credlayer-solana-fraud-gnn
ritesh-das/credlayer-solana-fraud-gnn
credlayer-solana-fraud-gnn is a tabular classification model from ritesh-das. Use it for the tabular classification task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
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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6.3 MB
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.pt6.3 MB · 100%
At a glance
- Task
- Tabular Classification
- License
- mit
- Model type
- fraud_gnn
- Access
- Public
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026
- SHA
- 048db0e5
Model card
From the Hugging Face model README
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.
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+
Dataset & Training Data
Trained on:
- SolRPDS (Solana Rug Pull Dataset): 62,895 suspicious liquidity pools and 22,195 confirmed rug-pull tokens derived from 3.69 billion transactions.
- Kaggle Solana Blockchain Dataset: Labeled entity categorization.
- Solarchive: Partitioned daily Solana Parquet transaction flows.
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 |
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.")
- Task
- Tabular Classification
- Type
- fraud_gnn
- License
- mit
- Languages
- en
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026