This is a graduate-level course where we will cover the latest research on graph analytics. Advanced undergraduates may enroll if they have taken 6.046 and 6.172. The course units are 3-0-9.

Lectures

Grading

Grading Breakdown Paper Questions and Reviews 20% Paper Presentations 25% Research Project 50% Class Participation 5%

Paper Questions and Reviews

For each lecture we will study several research papers; for each paper one student will give a presentation, which will be followed by a discussion.The assignments consist of writing weekly paper reviews, doing several paper presentations, class participation, and completing an open-ended research project. The grading breakdown is as follows.Prior to each lecture, you are expected to read the papers under "Required Reading" in the schedule for that lecture. Students are required to answer a posted question about each paper, due at 12:00pm on the day of the lecture.In addition, students are required to submit one paper review every week, due 11:59pm on Tuesdays. The review should be on a paper chosen from any of the starred papers (*) under "Required Reading" for the two lectures the week (i.e., the Wednesday and Friday immediately after the due date).The review should first describe the problem the paper is trying to solve, why it is important, the main ideas proposed, and the results obtained. The review should then describe how the ideas are novel compared to existing work at the time, the strengths and weaknesses of the paper, and any ideas you may have for improving the techniques and/or evaluation criteria, and any open problems or directions for further work that you can think of.

The answers to the paper questions as well as the paper reviews will be submitted on Learning Modules. The paper reviews will be made visible after each submission deadline, and you are encouraged to read other reviews to improve your understanding and to prepare for the class discussion.

Paper Presentations

Research Project

Students will give several presentations on assigned papers throughout the semester. These presentations should be 20 minutes long with slides, followed by a discussion. The presentation should discuss the motivation for the problem being solved, any definitions needed to understand the paper, key technical ideas in the paper, theoretical and/or experimental results, and existing work at the time (doing a literature search and including a discussion of recent related work would be a bonus). The presenter should also present his or her own thoughts on the paper (perceived strengths and weaknesses, directions for future work, etc.), and pose several questions for discussion. The presenter will be expected to lead the discussion on the paper.A large part of the work in this course is in proposing and completing an open-ended research project. This will give you a taste of doing research in the area of graph analytics. The project may involve (but is not limited to) any of the following tasks: implementation of non-trivial and theoretically-efficient graph algorithms; analyzing and optimizing the performance of existing graph codes; designing new graph algorithms that are theoretically and/or practically efficient; applying graph algorithms in the context of larger applications, e.g., in social network and Web analysis, machine learning, or computational biology; exploring new problems for analyzing the structure of graphs; improving or designing new graph processing frameworks/systems; and conducting a survey of a topic. The project may explore parallelism, cache-efficiency, I/O-efficiency, and memory-efficiency. The project can be related to research that you are currently doing, subject to instructor approval.

The project will be done in groups of 2-3 people and consist of a proposal, mid-term report, poster presentation, and final report. The timeline for the project is as follows.

Assignment Due Date Pre-proposal meeting 3/14 Proposal 3/16 Mid-term Report 4/13 Poster Session 5/14 Final Report 5/17

Pre-proposal meeting: You will schedule a 15 minute meeting with the instructor to discuss what you would like to propose. Feedback will be given to be incorporated into the proposal.

Proposal: The proposal should be about 2 pages long (excluding figures and references) and will describe the project that you are proposing to work on, the main components of the project, as well as a projected weekly schedule of what you plan to accomplish throughout the semester. The deadline is 3/16, but you may turn it in on 3/19 if you have an appointment at the Communication Lab to improve your proposal.

Mid-term report: The mid-term report will talk about what you have accomplished so far, a breakdown of the contribution among group members so far, any obstacles you encountered, any changes to the proposed tasks, and a schedule of the remaining work to be done. This should be about 6 pages long (excluding figures and references). The deadline is 4/13, you may turn it in on 4/16 if you have an appointment at the Communication Lab to improve your report.

Poster session: We will have a poster session on Monday 5/14 (time TBD), where you will describe your research to the instructor and classmates, and learn about other projects.

Final report: The final report will be in the style of a research paper describing your project. It should include an abstract summarizing the project, an introduction describing and motivating the problem, a brief discussion of related work, a brief overview of any background knowledge needed to understand the paper, followed by your contributions. It should also discuss any open problems or directions for further work, and include a breakdown of work among group members. The report should be about 10 pages long (excluding figures and references).

Piazza

Collaboration Policy

Computing Resources

Textbooks

Some Other Courses on Graph Analytics

We will be using Piazza for answering questions. You may publicly post questions about the papers or any cool ideas that you have about graph analytics. Discussions among students is encouraged. If possible, please use Piazza instead of emailing the course staff. You may use a private note if you want your post to be visible only to the course staff.You are welcome to read the papers with other students, but all of your written work should be your own. You should carefully acknowledge any collaborators and any outside sources you use to complete the paper questions, paper reviews, and the project reports. Please do not look at anyone else's answers before submitting your answers to the paper questions and your paper reviews.We will be using Amazon Web Services.Some of the readings will be from the following textbooks. Introduction to Algorithms, 3rd Edition by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein (CLRS) Networks, Crowds, and Markets by David Easley and Jon Kleinberg