NDLI: LR-PPR: locality-sensitive, re-use promoting, approximate personalized pagerank computation

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LR-PPR: locality-sensitive, re-use promoting, approximate personalized pagerank computation

Content Provider	ACM Digital Library
Author	Candan, K. Selçuk Kim, Jung Hyun Sapino, Maria Luisa
Abstract	Personalized PageRank (PPR) based measures of node proximity have been shown to be highly effective in many prediction and recommendation applications. The use of personalized PageRank for large graphs, however, is difficult due to its high computation cost. In this paper, we propose a Locality-sensitive, Re-use promoting, approximate personalized PageRank (LR-PPR) algorithm for efficiently computing the PPR values relying on the localities of the given seed nodes on the graph: (a) The LR-PPR algorithm is locality sensitive in the sense that it reduces the computational cost of the PPR computation process by focusing on the local neighborhoods of the seed nodes. (b) LR-PPR is re-use promoting in that instead of performing a monolithic computation for the given seed node set using the entire graph, LR-PPR divides the work into localities of the seeds and caches the intermediary results obtained during the computation. These cached results are then reused for future queries sharing seed nodes. Experiment results for different data sets and under different scenarios show that LR-PPR algorithm is highly-efficient and accurate.
Starting Page	1801
Ending Page	1806
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505651
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Locality-sensitivity Reuse-promotion Personalized pagerank
Content Type	Text
Resource Type	Article

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