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GCN for Cora Dataset

This project implements a Graph Convolutional Network (GCN) to classify papers in the Cora citation network dataset using PyTorch Geometric. The implementation is provided in a Jupyter Notebook.

Overview

  • Implements a GCN model for node classification
  • Uses the Cora dataset from PyTorch Geometric
  • Visualizes training loss and node embeddings

Requirements

  • Python 3.x
  • Jupyter Notebook
  • PyTorch
  • PyTorch Geometric
  • Matplotlib
  • Seaborn

Usage

  1. Install the required packages:

    pip install jupyter torch torch_geometric matplotlib seaborn

  2. Start Jupyter Notebook:

    jupyter notebook

  3. Open the Cora_gnn.ipynb file in the Jupyter Notebook interface.

  4. Run all cells in the notebook to train the model and generate visualizations.

Results

  • Test Accuracy: 0.703
  • The notebook generates and displays:
    • A plot of the training loss
    • A GIF showing the evolution of node embeddings

Visualizations

Training Loss

Training Loss

Node Embeddings Evolution

Node Embeddings

Future Work

  • Experiment with different GNN architectures
  • Apply to other citation network datasets

Author

chirag1701

License

This project is open-source and available under the MIT License.