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AN INNER-OUTER ITERATION FOR COMPUTING PAGERANK
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AI
DOI：10.1137/080727397.png)
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Abstract
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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 nonstationary 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.
Keywords:
damping factor
eigenvalues
inner-outer iterations
PageRank
power method
stationary schemes
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