OPERATIONS RESEARCH SEMINAR

Monte Carlo Methods for Top-k Personalized PageRank Lists with Application to Name Disambiguation

Wednesday, December 22
2:30-3:30
Math Tower 1-122

Konstantin Avrachenkov
INRIA - Sophia Antipolis, France

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 neighbors 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.

This seminar is partially supported by the Grad School.

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