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.
Overview
Syllabus
Lecture 1 Introduction; Structure of Graphs.
Lecture 2 Properties of Networks And Random Graph Models.
Lecture 3 Motifs and Structural Roles in Networks.
Lecture 4 Community Structure in Networks.
Lecture 5 Spectral Clustering.
Lecture 6 Message Passing and Node Classification.
Lecture 7 Graph Representation Learning.
Lecture 8 Graph Neural Networks.
Lecture 9 Graph Neural Networks Implementation with Pytorch Geometric.
Lecture 10 Deep Generative Models for Graphs.
Lecture 11 Link Analysis - PageRank.
Lecture 12 Network Effects and Cascading Behavior.
Lecture 13 Probabilistic Contagion and Models of Influence.
Lecture 14 Influence Maximization in Networks.
Lecture 15 Outbreak Detection in Networks.
Lecture 16 Network Evolution.
Lecture 17 Reasoning over Knowledge Graphs.
Lecture 18 Limitations of Graph Neural Networks.
Lecture 19 Applications of Graph Neural Networks.
Taught by
Hussain Kara Fallah
