Machine Learning with Graphs

(4 Credits)

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; social network analysis. Prerequisite: CSCI-SHU 360 Marchine Learning or MATH-SHU 235 Probability and Statistic Fulfillment: CS Electives, and DS course for AI concentration.