Topics in Statistics (359-0-21)

Topic

Deep Learning on Graphs

Instructors

Kaize Ding

Meeting Info

University Hall 122: Mon, Wed 2:00PM - 3:20PM

Overview of class

Topic: Deep Learning on Graphs
Graphs have been leveraged to denote data from various domains ranging from social science, linguistics to chemistry, biology, and physics. Meanwhile, numerous real-world applications can be treated as computational tasks on graphs. To facilitate these applications, a curial step is to learn good representations for graphs. More and more evidence has demonstrated that graph deep learning techniques especially graph neural networks (GNNs) have tremendously facilitated computational tasks on graphs. The revolutionary advances brought by GNNs have also immensely contributed to the depth and breadth of the adoption of graph representation learning in real-world applications.

This seminar course covers recent advances in the area of deep learning on graphs. More specifically, the following topics will be included: Network Embedding, Graph Neural Networks and Their Properties, Applications of GNNs, and others.

Registration Requirements

Pre-Requisite: 359-0 (UG/G) Topics in Statistics - Deep Learning or 362-0 (UG) Advanced Machine Learning for Data Science or 415-0 (G) Introduction to Machine Learning

Learning Objectives

Students are expected to read and discuss literature, make presentations, and work on related research projects.

Teaching Method

Lectures + Project + Presentation

Class Materials (Required)

None

Class Materials (Suggested)

Deep Learning on Graphs (Yao Ma, Jiliang Tang)

Graph Neural Networks: Foundations, Frontiers, and Applications (Lingfei Wu, etc)

Class Attributes

Formal Studies Distro Area