[Spring 2013] Information in Networks: Theory, Algorithms, and Applications

General Information

Instructor: Tina Eliassi-Rad Office hours: Mondays 2:00 PM - 3:00 PM in CBIM 08
Lecture Time: Mondays 3:20 PM - 6:20 PM Lecture Place: CBIM, Room 22
Course number: 16:198:672 Credits: 3


This graduate seminar will survey recent work in network science and computational social science from a machine learning and data mining perspective, covering topics such as: properties of real-world networks, graph models, network dynamics, information diffusion, collective classification, and community detection. Application domains discussed will range from social networks (coauthorship graphs) to information networks (the Web) to communication networks (email graphs, retweet graphs), and so on.

Prerequisites: Background in data mining and machine learning suggested. A basic course in probability and statistics required.



  1. Check the Resources folder of the class sakai site (16:198:672:01 Sp13) if you cannot find a resource on the Web.
  2. Upload your contributions (reaction notes, proposal report, presentation, final report, etc) to the class sakai site (16:198:672:01 Sp13).


These textbooks are recommended and not required.


Schedule / Syllabus (subject to change)