Network science is an exciting new field that studies large and complex networks, such as social, biological, and computer networks. The class will address topics from network structure and growth to the spread of epidemics. We study the diverse algorithmic techniques and mathematical models that are used to analyze such large networks, and give an indepth description of the theoretical results that underlie them.
Some topics are random graphs, giant components, power laws, percolation, spreading phenomena, community detection, basic algorithms for network science, lower bounds and advanced algorithms for polynomialtime problems, sampling algorithms, streaming algorithms, sublinear algorithms, and graph partitioning algorithms.
Course form
Lectures, tutorials, term paper, peer feedback.
Literature
Recommended: A. Barabasi, Network Science, for free online M.E.J. Newman, Networks, 2nd edition (2018).
The class is mostly based on the Barabasi book, with some parts taken from Newman. Using either book is sufficient for the class.
