Spring 2011: Graduate Seminar on Graph Mining and Network Analysis

General Information

Instructor: Tina Eliassi-Rad Office hours: Tuesdays 2:30-4:30 PM in CBIM 08
Time: Tuesdays 5:00-8:00 PM Place: Hill Center 254
Course number: 16:198:672 Credits: 3

Overview

This graduate seminar will survey recent work in network 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.

Grading

Notes

Upload your contributions (reaction notes, proposal report, presentation, final report, etc) to the class sakai site.

Resources

Schedule / Syllabus (Subject to Change)