I recommend this lecture from Stanford SNAP: “Machine Learning with Graphs”
Published:
For anyone interested in graphs, I highly recommend this lecture by Jure Leskovec and Michele Catasta from Stanford Network Analysis Project (SNAP). Checkout the course home page.
Here is a playlist of lectures from the archive of CS224W fall 2019, where slides are available.
- 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
Recommended reading from the course page
- Graph Representation Learning by William L. Hamilton
- Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg
- Network Science by Albert-László Barabási