LR-PPR: Locality-sensitive, re-use promoting, approximate personalized PageRank computation

Jung Hyun Kim, Kasim Candan, Maria Luisa Sapino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages1801-1806
Number of pages6
DOIs
StatePublished - Dec 11 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: Oct 27 2013Nov 1 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
CountryUnited States
CitySan Francisco, CA
Period10/27/1311/1/13

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Keywords

  • Locality-sensitivity
  • Personalized PageRank
  • Reuse-promotion

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

Cite this

Kim, J. H., Candan, K., & Sapino, M. L. (2013). LR-PPR: Locality-sensitive, re-use promoting, approximate personalized PageRank computation. In CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (pp. 1801-1806). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/2505515.2505651