Many modern complex networks such as Internet of Things, Online Social Networks, large scale wireless ad-hoc networks, power-grid, biological networks, etc. can be represented as a (large) graph. This course will first provide an introduction to graph modeling, its characterization, metrics on graph, ranking, and how to describe popular dynamics on graphs including random walk and epidemics. It also covers basics of graph sampling, property estimation, and randomized algorithms for computationally prohibitive (NP-hard) tasks such as clustering/partitioning. Applications include Google Pagerank algorithm, graph sampling, property estimation, identifying/ranking nodes, security, with connections to various statistical methods in machine learning and big (graph) data.
Tue &Thurs., 1229 EB2
Email: dyeun at ncsu dot edu
Prerequisites: Some background on linear algebra and basic probability will be helpful but not strictly required. Necessary background materials will be covered in class.
Note: Most lectures will be based on lecture notes and some of the references below
Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with Disability Services for Students. See https://dso.dasa.ncsu.edu/ for more information. more information on NC State's policy on working with students with disabilities, please see http://policies.ncsu.edu/regulation/reg-02-20-01
All the provisions of the NC State University's Code of Student Conduct and University Policy on Academic Integrity apply to this course. In addition, it is my understanding and expectation that your signature on any test or assignment means that you neither gave nor received unauthorized aid.