Distributed PageRank computation based on iterative aggregation-disaggregation methods

Abstract

PageRank has been widely used as a major factor in search engine ranking systems. However, global link graph information is required when computing PageRank, which causes prohibitive communication cost to achieve accurate results in distributed solution. In this paper, we propose a distributed PageRank computation algorithm based on iterative aggregation-disaggregation (IAD) method with Block Jacobi smoothing. The basic idea is divide-and-conquer. We treat each web site as a node to explore the block structure of hyperlinks. Local PageRank is computed by each node itself and then updated with a low communication cost with a coordinator. We prove the global convergence of the Block Jacobi method and then analyze the communication overhead and major advantages of our algorithm. Experiments on three real web graphs show that our method converges 5-7 times faster than the traditional Power method. We believe our work provides an efficient and practical distributed solution for PageRank on large scale Web graphs.