NDLI: BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart

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BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart

Content Provider	ACM Digital Library
Author	Park, Namyong Jung, Jinhong Kang, U Lee, Sael
Abstract	How can we measure similarity between nodes quickly and accurately on large graphs? Random walk with restart (RWR) provides a good measure, and has been used in various data mining applications including ranking, recommendation, link prediction and community detection. However, existing methods for computing RWR do not scale to large graphs containing billions of edges; iterative methods are slow in query time, and preprocessing methods require too much memory. In this paper, we propose BePI, a fast, memory-efficient, and scalable method for computing RWR on billion-scale graphs. BePI exploits the best properties from both preprocessing methods and iterative methods. BePI uses a block elimination approach, which is a preprocessing method, to enable fast query time. Also, BePI uses a preconditioned iterative method to decrease memory requirement. The performance of BePI is further improved by decreasing non-zeros of the matrix for the iterative method. Through extensive experiments, we show that BePI processes 100 times larger graphs, and requires up to 130 times less memory space than other preprocessing methods. In the query phase, BePI computes RWR scores up to 9 times faster than existing methods.
Starting Page	789
Ending Page	804
Page Count	16
File Format	PDF
ISBN	9781450341974
DOI	10.1145/3035918.3035950
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Random walk with restart Relevance score in graph
Content Type	Text
Resource Type	Article

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