Google PageRank and Reduced-Order Modelling

Google PageRank is designed to determine the importance of a webpage. To do so, one needs to compute an eigenvector of the Google matrix. We show that this vector can also be found by solving a linear system. Additionally, an adjustment to the PageRank model will be made by considering one of the parameters to be a stochastic variable. We will develop a reduced-order algorithm which is able to approximate the expected PageRank vector.Applied MathematicsDelft Institute of Applied MathematicsElectrical Engineering, Mathematics and Computer Scienc