Introduction to Graph Neural Networks and Deep Learning on Graphs

Traditional deep learning models excel at grid-like data like text and images, but real-world data often exists as complex networks of relationships. Graph Neural Networks (GNNs) bridge this gap by allowing you to apply powerful machine learning techniques directly to graph structures. This text-based course provides a clear, step-by-step introduction to representing, processing, and predicting on relational data. You will start with foundational graph theory and representation techniques before moving into core network architectures. By the end of this course, you will understand how information flows through nodes and edges, enabling you to design and evaluate GNN models for real-world applications. What you'll learn: - Understand foundational graph concepts including adjacency matrices, node features, and edge connections - Learn the mechanics of message passing, aggregation functions, and graph convolution techniques - Explore standard GNN architectures such as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) - Apply node classification, link prediction, and graph-level representation techniques - Implement basic GNN workflows using modern Python libraries and deep learning frameworks - Practice evaluating model performance and handling common graph learning challenges like over-smoothing This course begins with essential definitions and mathematical representations of graphs, ensuring you build a solid theoretical foundation. From there, you will progress through written explanations and code-based walk-throughs that demonstrate how to construct, train, and refine neural networks on graph-structured data. This course is designed for beginners in graph-based machine learning, data scientists, and developers with a basic understanding of Python and general machine learning concepts. No prior experience with graph theory or advanced network analysis is required. Start reading today to unlock the power of machine learning on relational and networked data.