CS 598 DEL

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Many modern data arrives in a form that is best represented by combinatorial structures such as graphs, rather than classical time series. Graphs show up in various examples and applications, ranging from social networks and internet graphs to biological data. Modeling the interaction between objects as a graph allows us to better understand, analyze, and predict the behavior of such networks. Such understanding is crucial in subsequent applications, including but not limited to estimation, learning, data compression, and community detection. The focus of this course is to study mathematical tools to analyze graphs, specifically random graphs as models for large graphical data. We further employ this analysis to discuss several applications such as epidemiology and learning. This is a graduate level course which is open to graduate students with a good level of mathematical maturity and a strong background in probability, as well as some basic background in graph theory. In this course