---
license: mit
library_name: pytorch
tags:
- graphconv
- citeseer
- node-classification
- graph-neural-network
---

# GRAPHCONV Node Classification on Citeseer

## Model Details
- **Architecture**: GRAPHCONV
- **Dataset**: Citeseer
- **Task**: Node Classification

## Usage

```python
from src.prediction import Predictor

# Load model
predictor = Predictor.from_checkpoint("best_model.pt")

# Make predictions
result = predictor.predict(graph_data)
print(f"Predictions: {result.predictions}")
print(f"Confidence: {result.confidence}")
```

## Files
- `best_model.pt` - Trained model checkpoint
- `hyperparameters.json` - Training configuration
- `experiment_summary.json` - Final metrics

## Training

```bash
python scripts/train.py \
    --dataset citeseer \
    --model graphconv \
    --epochs 150 \
    --lr_scheduler
```