JeffreyYAJ/Africa-Neighbourhood-GCN

This project explores the power of Graph Convolutional Networks (GCNs) applied to a custom-built dataset of African nations. # Africa-Neighbourhood-GCN # African Geo-GCN: Graph Neural Networks on Geopolitical Data ## The Concept This project explores the power of **Graph Convolutional Networks (GCNs)** applied to a custom-built dataset of African nations. Instead of treating countries as isolated data points (standard Machine Learning), we model the continent as a **Graph** where: * **Nodes** are countries. * **Edges** represent shared land borders. * **Node Features** are economic/demographic indicators (GDP, Population). The goal is to perform **Semi-Supervised Node Classification** to predict a country's geopolitical region based on its neighbors' influence, demonstrating how GNNs aggregate spatial information. ## Architecture (From Scratch) This implementation avoids high-level GNN libraries to focus on the mathematical mechanics of the **Kipf & Welling (2017)** propagation rule: $$ H^{(l+1)} = \sigma(\tilde{D}^{-\frac{1}{2}}\tilde{A}\tilde{D}^{-\frac{1}{2}} H^{(l)} W^{(l)}) $$ ### Project Structure * `src/models.py`: Custom PyTorch implementation of the Graph Convolution Layer. * `src/data.py`: Manual construction of the African Adjacency Matrix and Feature set. * `src/utils.py`: Spectral normalization of the adjacency matrix. ## How to Run 1. **Install Dependencies** ```bash pip install -r requirements.txt ``` 2. **Execute the Training** ```bash python main.py ``` ## Results The model is trained on a subset of countries and tested on "hidden" nodes (e.g., Kenya, Togo) to verify generalization. Input Features: GDP ($B), Population (M) Graph: 15 Nodes, Undirected. Test Accuracy: ~100% (Converges rapidly due to strong homophily in geopolitical borders). ## Visualization The training script automatically generates a visualization of the graph, coloring nodes by their predicted region. --- Created with passion for AI & Africa. **JeffreyYAJ**