Mathematics 333 - Structure of Networks

Network structures and network dynamics are a fundamental modern tool for modeling a broad range of problems from fields like economics, biology, physics, and sociology. Mathematical and machine learning techniques can be used to reveal underlying network structures. The course will use graphs (sets of nodes connected by edges) as a common language to describe networks and their properties. On the theoretical side, the course will cover topics such as basic probability, degree distribution, spectral graph theory (adjacency matrix, graph Laplacian), diffusion geometries, and random graph models. Applications will range over topics such as epidemics, marketing, prediction of new links in a social network, and game theory. The course will also include hands-on experiments and simulations. Three class meetings per week.

Requisite: MATH 271 or MATH 272 or instructor's permission. Limited to 24 students. Spring semester. Professor Yacoubou Djima.