document: A uniform approach to accelerated PageRank computation In this note we consider a simple reformulation of the traditional power iteration algorithm for computing the stationary distribution of a Markov chain . Rather than communicate their current probability values to their neighbors at each step , nodes instead communicate only changes in probability value . This reformulation enables a large degree of flexibility in the manner in which nodes update their values , leading to an array of optimizations and features , including faster convergence , efficient incremental updating , and a robust distributed implementation . While the spirit of many of these optimizations appear in previous literature , we observe several cases where this unification simplifies previous work , removing technical complications and extending their range of applicability . We implement and measure the performance of several optimizations on a sizable ( 34M node ) web subgraph , seeing significant composite performance gains , especially for the case of incremental recomputation after changes to the web graph .
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