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Title: Distributed PageRank computation with improved round complexities
Authors: Luo, Siqiang
Wu, Xiaowei
Kao, Ben
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Luo, S., Wu, X. & Kao, B. (2022). Distributed PageRank computation with improved round complexities. Information Sciences, 607, 109-125.
Project: RG18/21 
Journal: Information Sciences 
Abstract: PageRank is a classic measure that effectively evaluates the importance of nodes in large graphs. It has been applied in numerous applications spanning data mining, Web algorithms, recommendation systems, load balancing, search and connectivity structures identification. Computing PageRank for large graphs is challenging and this has motivated the studies of distributed algorithms to compute PageRank. Previously, little works have been spent on the distributed PageRank algorithms with strong guarantees on both complexity and accuracy. In this paper, we focus on the theoretical aspect and study the complexity of distributed PageRank computation based on the well-known congested-clique model with a bandwidth generalization. An existing algorithm proposed by Sarma et al. (2015) can be applied in this model, which estimates PageRanks in an n-node graph using, with high probability, O(logn) communication rounds and a bandwidth of O((logn)3) bits. We present Radar-Push (RP), which is a distributed PageRank algorithm that is strictly better in round complexities. Specifically, Radar-Push uses O(loglogn) communication rounds and an edge bandwidth of O((logn)3) bits. We further show that Radar-Push can be adapted to efficiently compute an important variant of PageRank, namely, the batch one-hop personalized PageRank, in O(loglogn) communication rounds.
ISSN: 0020-0255
DOI: 10.1016/j.ins.2022.05.108
Schools: School of Computer Science and Engineering 
Rights: © 2022 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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