CS224W - Machine Learning with Graphs
CS224W: Machine Learning with Graphs. Instructor: Prof. Jure Leskovec, Department of Computer Science, Stanford University. 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. You can find more information about this course, such as lecture slides and syllabus, here. (from Stanfordonline)
|Introduction: Machine Learning for Graphs|
|CS224W: Machine Learning with Graphs (Winter 2021)
Instructor: Prof. Jure Leskovec. Lecture Slides. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs.