Dynamic Network Analysis - PhD level # 17-801, Masters Level # 17-685, EPP # 19-640

Instructor: Dr. Kathleen M. Carley
Units: 12.0

Offered Spring 2022

Mondays and Wednesdays 3:05pm - 4:25pm
Recitation Fridays from 3:05pm - 4:25pm
Room Number: Virtually on Zoom and in Tepper 1101A

**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 analytics, like statistics, now plays a role in most empirical fields, and is a fundamental leg of data science.

Network science is a broad and multi-disciplinary field. In this class, students will: gain an appreciation of the history of the field; gain experience analyzing social, semantic, and trail based networks, gain an understanding of the difference between social networks, social media and artificial intelligence; the difference graph-based metrics for network analysis and graphical models; gain experience with the use of traditional and high dimensional network models, and the advances in this field. Applications and issues discussed will include: social media analytics, semantic networks, task networks, organizational design and teams, machine learning and network analysis, generative models, terrorism and crime, health, and fake news. 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: