This project provides a hands-on implementation of Recurrent Neural Networks (RNNs) and Graph Convolutional Networks (GCNs) using PyTorch. It covers:
- LSTM and GRU for Classification: Implements LSTM and GRU models for image classification on the FashionMNIST dataset.
- GCN for Node Classification: Implements a 2-layer GCN for node classification on the Cora citation network dataset. Explores the concept of heterophily and its impact on GCN performance.
- Graph Autoencoder (GAE) for Link Prediction: Implements a GAE using a GCN encoder for link prediction on the Cora dataset.
This project is ideal for anyone learning about RNNs, GCNs, and their applications in various machine learning tasks.
- LSTM/GRU for Classification:
- Run the code in section 1 to train and evaluate LSTM and GRU models on the FashionMNIST dataset.
- GCN for Node Classification:
- Run the code in section 2 to train and evaluate a GCN model on the Cora dataset.
- Explore the impact of heterophily by running the code in Task 10.
- GAE for Link Prediction:
- Run the code in Task 11 to train and evaluate a GAE for link prediction on the Cora dataset.
- RNN Implementations: Provides implementations of LSTM and GRU models from scratch.
- GCN Implementation: Implements a 2-layer GCN from scratch.
- Heterophily Analysis: Explores the concept of heterophily and its impact on GCN performance.
- Link Prediction with GAE: Demonstrates how to use a GCN encoder for link prediction in a GAE.
- Clear Explanations: Includes comments and explanations within the code to guide understanding.
- Visualization: Uses matplotlib to visualize training progress and results.
AISHWARYA NAYAK (Contributions are welcome! Feel free to open issues or submit pull requests.)