Course Websites

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.

Graph Algorithms for Community Structure Detection in Large Networks. Description: This is a course on applied algorithms, focusing on the use of discrete mathematics, graph theory, probability theory, statistics, machine learning, and simulations, to design and analyze algorithms for community detection, community search, and community extraction in large graphs (e.g., social networks, biological networks, and citation graphs) with millions of nodes, with the goal of making important breakthroughs in either theory or development of improved scalable methods. We will examine these questions from both a theoretical perspective (e.g., computational complexity and design of algorithms for hard optimization problems, resolution limit) as well as from a data-driven perspective. Of particular interest in this course are how well existing methods actually perform on large real-world and synthetic datasets in terms of cluster quality, which includes density, separability, and well-connectednes