CS785 Deep Learning on Graphs (Fall 2021)

General Information

Course Number: CS785
Lecture Hours: 10:00 am - 11:20 am, Tuesday and Thursday
Location: CKB226

Instructor

Yao Ma
Email: yao dot ma at njit dot edu
Office: GITC4204

Course Overview

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

Book

Deep Learning on Graphs (Yao Ma, Jiliang Tang), Cambridge University Press, 2021

• You can get a free pre-print copy here.

• You can pre-order for a physical copy here.

Paper Presentation

We will have several paper presentattion sessions through out the semester. Each student is required to present once.

• The presentation takes the form of oral presentations at a conference. Specifically, each talk is allocated for 25 minutes (20 minutes for presentation + 5 minutes for Q&A).

• A list of papers (usually on the same topic) will be provided for each session (The list will be released 3 weeks before the date of the corresponding session). You may choose one paper from the provided list for the presentation. You are also welcome to choose papers that are not in the list. Please send an email to me a week before the presentation if you would like to present a paper that are not in the lists.

• Please find the paper list at this page (you need to log in your NJIT account for access). Select the paper you would like to present by fill your name on the right of the paper.

Project

• The project can be carried out by a group of at most 3 students.

• You can choose any topics as long as they are related to graphs.

• Each group is required to submit a project proposal and a final report.

• Each group is required to give a Proposal Presentation (20 minutes) and a Final Presentation (25 minutes).

• More information about the project will be released at this page.

Report Review

• After all project reports are collected, each student will be assigned a report from other groups. The student is required to review this report and provide comments. More information on the review process will be released at this page.

Grading

• Class participation: 10%

• Paper Presentation: 20%

• Project: 60%

  • Proposal: 5%

  • Intermediate Presentation: 15%

  • Final Presentation: 20%

  • Report: 20% (10% review from other students + 10% review from the instructor)

• Report Review: 10%

Tentative Schedule

Academic Integrity

Academic Integrity is the cornerstone of higher education and is central to the ideals of this course and the university. Cheating is strictly prohibited and devalues the degree that you are working on. As a member of the NJIT community, it is your responsibility to protect your educational investment by knowing and following the academic code of integrity policy that is found at: http:www5.njit.edupoliciessitespoliciesfiles/academic-integrity-code.pdf.

Please note that it is my professional obligation and responsibility to report any academic misconduct to the Dean of Students Office. Any student found in violation of the code by cheating, plagiarizing or using any online software inappropriately will result in disciplinary action. This may include a failing grade of F, and/or suspension or dismissal from the university. If you have any questions about the code of Academic Integrity, please contact the Dean of Students Office at dos@njit.edu