Description
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 community detection, link prediction, dense subgraph discovery, finding graph motifs, and network centrality 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 analytics research, understand the state-of-the-art algorithms on various problems, and be familiar with the recent trends.
Prerequisites
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.
Grading Policy
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. Tentative schedule is below. A presentation is expected to be an hour long. Each week, all the students will read the papers of the week 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.
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 community detection, link prediction, dense subgraph discovery, finding graph motifs, and network centrality 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 analytics research, understand the state-of-the-art algorithms on various problems, and be familiar with the recent trends.
Prerequisites
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.
Grading Policy
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. Tentative schedule is below. A presentation is expected to be an hour long. Each week, all the students will read the papers of the week 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.
General Information
Class webpage
Staff Office Hours
A. Erdem Sariyuce
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