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1 MAESTRO - Models for the performance analysis and the control of networks CRISAM - Inria Sophia Antipolis - Méditerranée 2 EEMCS - Faculty of Electrical Engineering, Mathematics and Computer Science Twente

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.

keyword : Personalized PageRank Monte Carlo Methods Person Name Disambiguation





Author: Konstantin Avrachenkov - Nelly Litvak - Danil Nemirovsky - Elena Smirnova - Marina Sokol -

Source: https://hal.archives-ouvertes.fr/



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