MA 577 Statistical Network Analysis
In recent decades, techniques for analyzing complex systems with relational structures have yielded new insights in diverse domains including sociology, biology, economics, and neuroscience. The scale and inherently interdependent structure of networks demands a novel toolkit for summarizing, visualizing, and exploring these datasets. This class introduces theory and methods for analyzing network indexed data, with a particular emphasis on real-world data analysis. Topics include theoretical treatments of random graph models and network processes, tools for inference based on common network properties, and demonstrations of computational procedures. Applications from various fields are presented throughout the class, and students will be required to analyze a large network dataset of their choosing. Basic familiarity with Python or R is expected.
Prerequisite
Graduate Student or At Least Junior
Distribution
Pure and Applied Mathematics Program