Graph Machine Learning

This lecture explores the application of graph-enhanced machine learning in various industries and academia, focusing on fraud detection, malware detection, and recommendation systems. It delves into the challenges of applying deep learning to non-Euclidean domains like graphs and manifolds, presenting PinSage, a graph convolutional network for web-scale recommender systems. The lecture discusses vertex and graph embedding techniques, graph queries, and the importance of relational inductive biases in deep learning architectures for achieving human-like AI abilities.