Monte Carlo methods for top-k personalized PageRank lists and name disambiguation

Konstatin Avrachenkov, Nelli Litvak, Danil Nemirovsky, Elena Smirnova, Marina Sokol

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Abstract

We study a problem of quick detection of top-k Personalized PageRank lists. This problem has a number of important applications such as finding local cuts in large graphs, estimation of similarity distance and name disambiguation. In particular, we apply our results to construct efficient algorithms for the person name disambiguation problem. We argue that when finding top-k Personalized PageRank lists two observations are important. Firstly, it is crucial that we detect fast the top-k most important neighbours of a node, while the exact order in the top-k list as well as the exact values of PageRank are by far not so crucial. Secondly, a little number of wrong elements in top-k lists do not really degrade the quality of top-k lists, but it can lead to significant computational saving. Based on these two key observations we propose Monte Carlo methods for fast detection of top-k Personalized PageRank lists. We provide performance evaluation of the proposed methods and supply stopping criteria. Then, we apply the methods to the person name disambiguation problem. The developed algorithm for the person name disambiguation problem has achieved the second place in the WePS 2010 competition.
Original languageUndefined
PublisherINRIA
Number of pages28
Publication statusPublished - Aug 2010

Publication series

NameResearch Report / INRIA, ISSN 0249-6399
PublisherINRIA
No.7367
ISSN (Print)1874-4850

Keywords

  • Person name disambiguation
  • Personalized PageRank
  • Monte Carlo Methods
  • IR-80246

Cite this

Avrachenkov, K., Litvak, N., Nemirovsky, D., Smirnova, E., & Sokol, M. (2010). Monte Carlo methods for top-k personalized PageRank lists and name disambiguation. (Research Report / INRIA, ISSN 0249-6399; No. 7367). INRIA.