A complete implementation and comparison of three state-of-the-art Graph Neural Network architectures: GCN, GraphSAGE, and GAT on the Cora citation network dataset.
🎯 Project Overview
This project demonstrates the implementation and comparative analysis of Graph Neural Networks for node classification tasks. Using the Cora citation network dataset, we train and evaluate three different GNN architectures to understand their strengths and performance characteristics.
Key Results
🥇 GAT (Graph Attention Networks): 81.9% test accuracy
🥈 GCN (Graph Convolutional Networks): 79.3% test accuracy
🥉 GraphSAGE: 76.8% test accuracy
📊 Dataset: Cora Citation Network
2,708 nodes (machine learning papers)
10,556 edges (citation relationships)
1,433 features per node (bag-of-words from abstracts)
📈 Visualization: Training curves, t-SNE embeddings, network structure
🛡️ Robust Training: Early stopping, model checkpointing, cross-platform compatibility
📝 Detailed Logging: Complete training logs instead of code comments
💾 Artifact Saving: Models, results, and visualizations saved automatically
🗂️ Project Structure
├── gnn_comparison.py # Main training script
├── best_*_model.pth # Best model checkpoints
├── *_full_model.pkl # Complete model objects
├── training_curves.png # Loss and accuracy visualizations
├── embeddings_tsne.png # t-SNE embedding visualizations
├── graph_visualization.png # Network structure visualization
├── results_summary.json # Comprehensive metrics
├── gnn_training.log # Complete training logs
└── README.md # This file
🧪 Methodology
Model Architectures
Graph Convolutional Networks (GCN)
Spectral approach to graph convolutions
Simple and effective baseline
Fast convergence with good performance
GraphSAGE (Sample and Aggregate)
Sampling-based approach for scalability
Inductive learning capability
Handles large graphs efficiently
Graph Attention Networks (GAT)
Multi-head attention mechanism
Dynamic neighbor weighting
Best performance but highest complexity
Training Configuration
python
1config ={2'hidden_dim':32,# Compact representation3'num_layers':2,# Avoids over-smoothing4'dropout':0.5,# Strong regularization5'learning_rate':0.001,# Conservative learning6'weight_decay':5e-4,# L2 regularization7'epochs':200,# Maximum training8'patience':20,# Early stopping9'attention_heads':8# Multi-head attention (GAT)10}
📊 Results Analysis
Performance Metrics
Model
Test Acc
Precision
Recall
F1-Score
Parameters
GCN
79.3%
0.791
0.793
0.792
46K
GraphSAGE
76.8%
0.765
0.768
0.766
92K
GAT
81.9%
0.819
0.819
0.819
369K
Key Insights
GAT's Superior Performance: Attention mechanism provides significant advantage
Efficiency vs Performance: GCN offers good performance with fewer parameters
Convergence Speed: GAT converges fastest despite higher complexity
Regularization Impact: Strong dropout (0.5) crucial for small training set
🎨 Visualizations Generated
The project automatically generates comprehensive visualizations:
1. Network Structure Visualization
Graph Structure
Shows the Cora citation network with:
Nodes colored by research area (7 classes)
Spring layout for optimal visualization
Clear community structure visible
2. Training Progress Monitoring
Training Curves
Displays for each model:
Loss curves: Training and validation loss progression
Accuracy curves: Training and validation accuracy trends
Overfitting analysis: Gap between train/validation performance
3. Learned Representation Quality
Node Embeddings
t-SNE visualization showing:
Class separation: How well models distinguish between research areas
Embedding quality: Clustering strength in learned representations
Model comparison: Visual comparison of representation learning
🛠️ Technical Details
Device Compatibility
Apple Silicon MPS: Automatic detection and acceleration
NVIDIA CUDA: GPU acceleration support
CPU Fallback: Universal compatibility
Best Practices Implemented
Early stopping to prevent overfitting
Model checkpointing for reproducibility
Comprehensive logging for debugging
Cross-platform compatibility
Memory-efficient implementations
📚 Learning Outcomes
This implementation demonstrates:
Graph Neural Network Fundamentals
Message passing framework
Neighborhood aggregation
Semi-supervised node classification
Architecture Comparison
Spectral vs spatial approaches
Attention mechanisms in graphs
Scalability considerations
Best Practices
Hyperparameter selection for graphs
Regularization techniques
Evaluation methodologies
🔧 Reproducibility
All experiments are fully reproducible:
Fixed random seeds for consistent results
Complete configuration saved in results_summary.json
Model checkpoints saved at best validation performance
Comprehensive logging of all training steps
🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
