Skip to Main content Skip to Navigation
Reports

Monte Carlo Methods for Top-k Personalized PageRank Lists and Name Disambiguation

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.
Complete list of metadata

Cited literature [30 references]  Display  Hide  Download

https://hal.inria.fr/inria-00510991
Contributor : Konstantin Avrachenkov Connect in order to contact the contributor
Submitted on : Monday, August 23, 2010 - 10:08:57 AM
Last modification on : Thursday, January 20, 2022 - 5:27:38 PM
Long-term archiving on: : Wednesday, November 24, 2010 - 2:58:22 AM

Files

RR-7367.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00510991, version 1
  • ARXIV : 1008.3775

Collections

Citation

Konstantin Avrachenkov, Nelly Litvak, Danil Nemirovsky, Elena Smirnova, Marina Sokol. Monte Carlo Methods for Top-k Personalized PageRank Lists and Name Disambiguation. [Research Report] RR-7367, INRIA. 2010. ⟨inria-00510991⟩

Share

Metrics

Record views

151

Files downloads

453