Dynamic Network Analysis - PhD level # 08-801

Instructor: Dr. Kathleen M. Carley
Units: 12.0

Offered Spring 2017


Mondays and Wednesdays 1:30pm - 2:30pm
Recitation Wednesdays from 4:30pm - 5:20pm
Room Number: GHC (Gates Hillman Complex) 4102
Course Syllabus

**The course Dynamic Network Analysis can be counted as an elective for security students in Information Security Policy & Management (MSISPM).

Course Description:

Who knows who? Who knows what? Who is influential? What is the social network, the knowledge network, the activity network? How do ideas, products & diseases propagate through groups and impact these networks? Does social media change the way these networks operate? Questions such as these & millions of others require a network perspective and an understanding of how ties among people, ideas, things, & locations connect, constrain & enable activity. In the past decade there has been an explosion of interest in network science moving from the work on social networks and graph theory to statistical and computer simulation models. Network analysis, like statistics, now plays an role in most empirical fields.

This course provides insight into this broad and growing field from a cross-disciplinary perspective. Fundamental metrics and advanced methods are covered, with attention to the application areas where these can and have been used. In class projects will cover the application and development of techniques for analyzing a range of networks including, but not limited to, social networks, social-media networks (e.g. twitter networks), geo-spatial constraints on networks, dynamic networks, semantic networks, and alliance networks. Methods for network data collection, analysis, visualization, and interpretation are covered. Students produce original research in which network data is analyzed using the methods covered in the class.

Previously taught:

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