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CS224W - Machine Learning with Graphs

Last updated Aug 31, 2022

Course Instructor:: Jurij Leskovec

Description

This course covers important research on the structure and analysis of such large social and information networks and on models and algorithms that abstract their basic properties. Students will explore how to practically analyze large-scale network data and how to reason about it through models for network structure and evolution.

CS224W | Home (stanford.edu)

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.

Prerequisites

  • 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)

Materials

The following books are recommended as optional reading:

# Lectures