TA: Sherry (Mengjiao) Yang

Schedule: Wednesdays and Fridays 2:30-4pm in room 56-154

Office hours: By appointment

Emails: jshun AT mit.edu, mengjiao AT mit.edu (please try to use Piazza)

Units: 3-0-9

Prerequisites: 6.046, 6.172

Graph analytics have applications in a variety of domains, such as social network and Web analysis, computational biology, machine learning, and computer networking. This course will cover research topics in graph analytics including algorithms, optimizations, frameworks, and applications. Students will learn about both the theory and practice of designing efficient graph algorithms (parallel, cache-efficient, external-memory, etc.). We will also study design choices in high-level graph processing frameworks. Students will read and present research papers, and also complete a research project. This course is suitable for graduate students or advanced undergraduates who have taken 6.046 and 6.172/6.871.

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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.

Grading Breakdown | |
---|---|

Paper Questions and Reviews | 20% |

Paper Presentations | 25% |

Research Project | 50% |

Class Participation | 5% |

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.

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).

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

Analysis of Networks (Jure Leskovec, Stanford)

Networks (David Easley and Jon Kleinberg, Cornell)

Topics in Social Data (Johan Ugander, Stanford)

Network Theory (Mark Newman, University of Michigan)

Graphs and Networks (Dan Spielman, Yale)

Statistical Network Analysis (Jennifer Neville, Purdue)

Network Analysis and Modeling (Aaron Clauset, Sante Fe Institute)

Parallel Graph Analysis (George Slota, RPI)

Large-Scale Graph Mining (A. Erdem Sariyuce, University of Buffalo)

Mining Large-scale Graph Data (Danai Koutra, University of Michigan)

Data Mining meets Graph Mining (Leman Akoglu, Stony Brook)

Graphs and Networks (Charalampos Tsourakakis, Aalto University)

Large-Scale Graph Processing (Keval Vora, Simon Fraser University)