Spring 2016: Network Science Research

General Information

Course acronym & number: NETS 8984


 Instructor: Tina Eliassi-Rad

Lecture time: Tuesdays 1:30 – 3:30 PM


 Office hours: Tuesdays 3:30 – 4:30 PM

Lecture location: 10th Floor Conference Room, 177 Huntington Ave, Boston, MA


 Office location: 10th Floor, 177 Huntington Ave, Boston, MA

Credits: 2


 Email: tina (at) eliassi (dot) org


This PhD-level course will cover state-of-the-art models and algorithms for learning and mining large-scale networks.


This course does not have a designated textbook.  The readings are assigned in the syllabus.


o   Class participation (20%): When reading a paper consider these questions:

a)     What is the problem being addressed?

b)    What are the main technical contributions?

c)     What are the weaknesses of the paper, how could they be improved?

d)    What are some of the promising research questions, and how could they be pursued?

o   Semester-long class project (80%):  The content and scope of the project will be decided in consultation with the instructor.  The project can be on the student’s PhD dissertation.  At the end of the semester, each student will present his/her project to the class and submit a report (at most 10 pages long) in the ACM formatting guidelines.  For guidance on writing the final report, please see Slide 70 of Eamonn Keogh's KDD'09 Tutorial on How to do good research, get it published in SIGKDD and get it cited!


o   Eliassi-Rad’s Resources Page

o   Mathworks Matlab Tutorials

o   Ben Taskar's Matlab Tutorial

o   Probability Review (David Blei, Princeton)

o   Probability Theory Review (Arian Maleki and Tom Do, Stanford)

o   Linear Algebra Tutorial (C.T. Abdallah, Penn)

o   Linear Algebra Review and Reference (Zico Kolter and Chuong Do, Stanford)

o   Statistical Data Mining Tutorials (Andrew Moore, Google/CMU)

o   Theoretical CS Cheat Sheet (Princeton)

Schedule / Syllabus (Subject to Change)

Notes and Policies

o   When emailing me, please begin the subject line with [sp16 nets].

o   For your class project, you can use whatever programming language you like.

o   Refresh your knowledge of the university's academic integrity policy and plagiarism. There is zero-tolerance for cheating!