This study group meets every Sunday from 10:00 AM – 11:00 AM PT starting on June 12, 2021. You can join our slack community by clicking “JOIN US” at twimlai.com/community. The study group slack channel is #graph_ml_cs224w.
About the Course
Complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks.
Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis.
Students are expected to have the following background:
- Knowledge of basic computer science principles, sufficient to write a reasonably non-trivial computer program (e.g., CS107 or
- CS145 or equivalent are recommended)
- Familiarity with the basic probability theory (CS109 or Stat116 are sufficient but not necessary)
- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary)
- The recitation sessions in the first weeks of the class will give an overview of the expected background.
Notes and reading assignments will be posted periodically on the course Web site. The following books are recommended as optional reading:
- 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