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