Course Description:

The course provides an introduction to complex network theory and its applications in physics, biology, technology and social sciences. Basic graph theory and the statistical physics foundations as well as applications to real world networks will be covered. A hands-on approach to analytical and computational techniques for real world networks will be provided. Essential network models, e.g. small world networks, scale free networks, spatial and hierarchical networks will be discussed and methods to generate them with a computer will be covered. Different  network visualization techniques and complex network tools will be explored as well. The course will cover three main branches of network science: 1.) Network structure, 2.) Dynamical processes on networks, and 3.) Network evolution.

Course Prerequisites:

Calculus through Math 234, EA1-EA4 or Math 240 and Math 250 and a knowledge of programming, e.g. Matlab.