Dive into the world of machine learning with graphs through this comprehensive lecture series from Stanford University. Explore a wide range of topics, including graph structures, network properties, random graph models, community detection, spectral clustering, and graph neural networks. Learn about message passing, node classification, graph representation learning, and deep generative models for graphs. Discover link analysis techniques like PageRank, network effects, cascading behavior, and influence maximization. Investigate probabilistic contagion, outbreak detection, network evolution, and reasoning over knowledge graphs. Gain practical implementation skills using PyTorch Geometric and examine the applications and limitations of graph neural networks in real-world scenarios.