Fast PageRank approximation by adaptive sampling

Abstract

PageRank is typically computed from the power of transition matrix in a Markov Chain model. It is therefore computationally expensive, and efficient approximation methods to accelerate the computation are necessary, especially when it comes to large graphs. In this paper, we propose two sampling algorithms for PageRank efficient approximation: Direct sampling and Adaptive sampling. Both methods sample the transition matrix and use the sample in PageRank computation. Direct sampling method samples the transition matrix once and uses the sample directly in PageRank computation, whereas adaptive sampling method samples the transition matrix multiple times with an adaptive sample rate which is adjusted iteratively as the computing procedure proceeds. This adaptive sample rate is designed for a good trade-off between accuracy and efficiency for PageRank approximation. We provide detailed theoretical analysis on the error bounds of both methods. We also compare them with several state-of-the-art PageRank approximation methods, including power extrapolation and inner–outer power iteration algorithm. Experimental results on several real-world datasets show that our methods can achieve significantly higher efficiency while attaining comparable accuracy than state-of-the-art methods.