An Inner-Outer Iteration for Computing PageRank

We present a new iterative scheme for PageRank computation. The algorithm is applied to the linear system formulation of the problem, using inner-outer stationary iterations. It is simple, can be easily implemented and parallelized, and requires minimal storage overhead. Our convergence analysis shows that the algorithm is effective for a crude inner tolerance and is not sensitive to the choice of the parameters involved. The same idea can be used as a preconditioning technique for non-stationary schemes. Numerical examples featuring matrices of dimensions exceeding 100,000,000 in sequential and parallel environments demonstrate the merits of our technique. Our code is available online for viewing and testing, along with several large scale examples. Key words. damping factor, eigenvalues, inner-outer iterations, PageRank, power method, stationary schemes AMS subject classifications. 65F10, 65F15, 65C40

• Type: Journal
• Year: 2010
• Where: SIAMSC
• Authors: David F. Gleich, Andrew P. Gray, Chen Greif, Tracy Lau
• Keywords: Crude Inner Tolerance, Inner-outer Stationary Iterations, Large Scale Examples, SIAMSC 2010