Spring 2023 Course Descriptions
CS 150-02 Deep Graph Learning
MW 3:00-4:15, Tisch Library 316
Graph and network data are ubiquitous and often have a large scale. Graph data are generally characterized by the graph structure and data attached to graph nodes or edges. Machine learning is an important approach to automated information extraction from graph data. However, graph data need special model designs, as most learning models (e.g., neural networks) only accept vectors as the input. In this course, we will introduce a list of models that can extract information from graph data. Furthermore, we will also discuss the principles behind these models from the perspective of graph theory. The coursework consists of quizzes, 3 projects, and a final project.