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.

It is assumed that students have a solid background on discrete mathematics and algorithms. Basic research skills like paper reading, critical thinking, problem solving, report writing, communication, and presentation are important as well.

This course is based on the research papers on large-scale graph mining (papers discussed in Fall'18 can be found

here). No textbooks required.

** 1 or 2 credits **
- Paper presentation: 40%
- Piazza questions: 30%
- Class participation: 30%

** 3 credits **
- Paper presentation: 40%
- Piazza questions: 20%
- Class participation: 20%
- Literature survey: 20%

The final grade is S/U and 75% score is needed for an S. Regarding the literature survey (for students taking 3 credits), topic will be decided with instructor.

**Paper presentation & Questions:** Each student picks 1-2 papers from the reading list and present. A presentation is expected to be an hour. Students must read all the papers presented. Each week, all the students will read the presented paper before class and will ask

**a unique question** on Piazza (except the presenter) to facilitate a class discussion. Questions should be open-ended and provide ground for class discussions, i.e., 'can you explain alg 1?' is not that kind of question. Questions should be posted to Piazza by Monday night, 11.59 pm EST.