Over the past decade, there has been a growing interest for the complex "connectedness" of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place. Beyond this classical example, the Network science is a now thriving and increasingly important cross-disciplinary domain that focuses on the representation, analysis, and modeling of various connected systems such as social network, mobility and transport networks. Motivated by these developments in the world, there has been a coming-together of multiple scientific disciplines in an effort to understand how highly connected systems operate. Network science aims to capture, modeling and understanding networks and rich data requires understanding the computational tools for identifying and explaining the patterns they contain. This graduate-level course will examine modern techniques for analyzing and modeling the structure and dynamics of complex networks. The focus will be on statistical algorithms and methods, and both lectures and assignments will emphasize model interpretability and understanding the processes that generate real data. Applications will be drawn from computational biology and computational social science.
