Node Classification with PyTorch Geometric and Graph Neural Networks

Graph-structured data is highly effective for representing complex biological systems, yet analyzing these networks requires specialized machine learning techniques. This course provides a clear, step-by-step pathway to understanding and solving node classification problems using Graph Neural Networks (GNNs). You will start with the core concepts of graph theory and neural architectures before moving into practical implementation. Learn how to prepare biological datasets, construct GNN models, and evaluate their performance using industry-standard metrics. What you'll learn: Understand the foundational concepts of graph structures, nodes, and edges; Load and preprocess biological graph data using PyTorch Geometric; Implement Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs); Configure training loops and optimize hyperparameters for graph models; Apply evaluation metrics to measure classification accuracy on imbalanced datasets. The course begins with essential terminology and data representation basics, gradually building up to complete training and evaluation workflows for node classification. This course is designed for beginners in graph machine learning, data scientists, and bioinformatics enthusiasts with a basic understanding of Python. Start reading today to master the fundamentals of modern graph neural networks.