Analysis of Networks

Networks are a fundamental tool for modeling complex social, technological, and biological systems. Coupled with emergence of online social networks and large-scale data availability in biological sciences, this course focuses on the analysis of massive networks which provide many computational, algorithmic, and modeling challenges. This course will cover recent research on the structure and analysis of such large networks and on models and algorithms that abstract their basic properties. The course will explore how to practically analyze large scale network data and how to reason about it through models for network structure and evolution. Topics include: how information spreads through society; robustness and fragility of food webs and financial markets; algorithms for the World Wide Web; friend prediction in online social networks; identification of functional modules in biological networks; disease outbreak protection. This course serves as an introductory course in the mathematics of MATHEMATICS & COMPUTER SCIENCE (62) 201 quantum information theory. Topics include quantum states, quantum channels, information measures and measurements, entanglement, and entropy, as well as the mathematical preliminaries necessary to discuss these topics.