Efficient processing node proximity via random walk with restart

Bingqing Lv, Weiren Yu, Liping Wang, Julie A. McCann

Research output: Chapter in Book/Published conference outputConference publication


Graph is a useful tool to model complicated data structures. One important task in graph analysis is assessing node proximity based on graph topology. Recently, Random Walk with Restart (RWR) tends to pop up as a promising measure of node proximity, due to its proliferative applications in e.g. recommender systems, and image segmentation. However, the best-known algorithm for computing RWR resorts to a large LU matrix factorization on an entire graph, which is cost-inhibitive. In this paper, we propose hybrid techniques to efficiently compute RWR. First, a novel divide-and-conquer paradigm is designed, aiming to convert the large LU decomposition into small triangular matrix operations recursively on several partitioned subgraphs. Then, on every subgraph, a “sparse accelerator” is devised to further reduce the time of RWR without any sacrifice in accuracy. Our experimental results on real and synthetic datasets show that our approach outperforms the baseline algorithms by at least one constant factor without loss of exactness.
Original languageEnglish
Title of host publicationWeb Technologies and Applications
Subtitle of host publication16th Asia-Pacific Web Conference, APWeb 2014, Changsha, China, September 5-7, 2014. Proceedings
EditorsLei Chen, Yan Jia, Timos Sellis, Guanfeng Liu
Place of PublicationCham (CH)
Number of pages8
ISBN (Electronic)978-3-319-11116-2
ISBN (Print)978-3-319-11115-5
Publication statusPublished - 2014
Event16th Asia-Pacific Web Conference - Changsha, China
Duration: 5 Sept 20147 Sept 2014

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference16th Asia-Pacific Web Conference
Abbreviated titleAPWeb 2014


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