Graph neural networks have shown great promise in learning representations of graph-structured data, which can improve the accuracy of downstream machine-learning tasks. The model presented in this blog post harnesses the power of graph neural networks to capture and encode the relationships between data points and enhance document classification accuracy. By leveraging word embeddings as input features, the GraphSAGE algorithm iteratively aggregates information from neighboring nodes, resulting in powerful node-level representations that can improve the accuracy of downstream machine learning models. The model's performance is enhanced by considering the relationships between articles, leading to improved precision and weighted precision compared to traditional word embedding models.