Introduction to Graph Embeddings and Representation Learning

Graphs are everywhere, from social networks to molecular structures, but feeding this complex relational data into machine learning models requires converting it into vector spaces. Understanding graph embeddings is the key to unlocking powerful predictive capabilities for networked data. This course guides you from the fundamental mathematics of network science to the practical application of representation learning, ensuring you gain a solid conceptual foundation while reinforcing your knowledge with built-in written assessments. What you'll learn: - Understand the core principles of graph theory and why traditional machine learning struggles with network topology. - Learn how classic algorithms like DeepWalk and Node2Vec map nodes into low-dimensional vector spaces. - Explore the foundations of Graph Convolutional Networks and modern message-passing frameworks. - Apply evaluation metrics to assess the quality of your embeddings for link prediction and node classification. - Discover how to store and query graph embeddings using modern vector databases. - Practice your comprehension through structured conceptual questions and step-by-step written code walkthroughs. The course begins with essential terminology, defining graphs, adjacency matrices, and embedding spaces. You will then progress to random walk methods, neural graph architectures, and modern evaluation techniques, ensuring a complete grasp of how to represent relational data. Designed for aspiring data scientists, machine learning beginners, and software engineers, this text-only course requires no advanced prerequisites other than a basic familiarity with programming concepts. Start reading today to master the fundamentals of graph representation learning.