Network Science Analytics (ECE 442/DSC 422) is graduate class about networks. The science of networks is an emerging discipline of great importance that combines graph theory, probability and statistics, and facets of engineering and the social sciences. Topics in this course will help answer intriguing questions such as: Where does "six degrees of separation" come from? How can we make sense of large graphs, ranging from social networks to the smart power grid? What are the underpinnings of Google's search engine and webpage ranking? What are good models for predicting popularity in Twitter? How can we estimate the size of the Internet?
This course will provide students with the mathematical tools and computational training to understand large-scale networks in the current era of Big Data. It will introduce basic network models and structural descriptors, network dynamics and prediction of processes evolving on graphs, modern algorithms for topology inference, community and anomaly detection, as well as fundamentals of social network analysis. All concepts and theories will be illustrated with numerous applications and hands-on case studies from technological, social, biological, and information networks.
When: Mondays and Wednesdays 3:25pm - 4:40pm.
Where: Computer Studies Building (CSB) 601. Textbook: We will use lecture slides to cover the material. A book I will follow for the class is
Eric D. Kolaczyk, "Statistical Analysis of Network Data: Methods and Models," Springer.
The book can be obtained online from the University of Rochester libraries here.
In addition to research papers posted by the instructor, supplementary recommended bibliography includes
M. E. J. Newman, "Networks: An Introduction," Oxford University Press. Available online from UR libraries
W. L. Hamilton, "Graph Representation Learning," Morgan and Claypool. Available online
D. Easley and J. Kleinberg, "Networks, Crowds, and Markets: Reasoning About a Highly Connected World," Cambridge University Press. Available online
J. Leskovec, A. Rajaraman and J. D. Ullman, "Mining of Massive Datasets," Cambridge University Press. Available online
These books are on reserve for the class in Carlson Library.
Prerequisites: Useful to have good background in probability theory and linear
algebra, as well as some basic exposure to graphs and optimization theory.
For homework assignments we will use Matlab (see the user guide
here) or Python;
and possibly NetworkX and PyTorch Geometric.
Credit distribution: Homework assignments (~3-5, 30%), and a research project
involving three deliverables (proposal 10%, progress report 10%, final report and
in-class presentation 50%). Detailed information on the project will be posted here.