Course Description

Graphs are everywhere. Their scale, rate of change, and the irregular nature pose many new challenges. This seminar course covers a range of topics about the practical algorithms that enable fast graph analytics for the real-world data. We focus on different types of algorithms such as dense subgraph discovery, finding graph motifs, and the community detection by considering the characteristics of the real-world data which can be large, distributed, streaming, noisy, and incomplete. Students will learn the literature on graph mining research, understand the state-of-the-art algorithms on various problems, and be familiar with the recent trends.


As a research seminar, it is assumed that students have a solid background on discrete mathematics, and strong coding skills. Course project is an important part of the class, and the basic research skills like paper reading, critique, critical thinking, problem solving, report writing, communication, and presentation are important.

Course Materials

This course is based on the research papers from top data mining and databases conferences, such as SIGKDD, WWW, VLDB, SIGMOD and more. Papers that will be discussed can be found here. No textbooks required.

Grading Policy

  • Paper presentation: 30%
  • Critiques & Questions: 30%
  • Project: 40%
Note that the final grade is S/U and at least 75% score is needed for an S. Course workload is same for any number of credits. That includes the project, paper presentations, critiques and questions.

Paper presentation, Critiques & Questions:
Each student picks 1-2 papers from the reading list and present. We will discuss one paper in each class. A presentation can be up to 45 mins. Each week, students will write a short critique and ask a question (except the presenter). Critiques should be emailed in plain text to instructor by Monday night, 11.59 pm EST. Questions should be posted to Piazza by the same deadline.

Teams of 1 or 2. The content and scope of the project will be decided in consultation with the instructor. Each team will meet and update the instructor every other week. At the end of the semester, each team will present their project to the class and submit a report (at most 9 pages) in the ACM format.

To ensure a healthy execution, please check the following deadlines.
  • Sep 6: Team signup
  • Sep 13: Project topic selection