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Avrachenkov, K. and Litvak, N. and Nemirovsky, D. and Smirnova, E. and Sokol, M.
(2011)
Quick detection of top-k personalized PageRank lists.
In: 8th International Workshop on Algorithms and Models for the Web Graph, WAW 2011, 27-29 May 2011, Atlanta, GA, USA.
pp. 50-61.
Lecture Notes in Computer Science 6732 (2011).
Springer.
ISBN 978-3-642-21286-4
Full text available as:
Official URL: http://dx.doi.org/10.1007/978-3-642-21286-4_5  AbstractWe study a problem of quick detection of top-k Personalized PageRank (PPR) lists. This problem has a number of important applications such as finding local cuts in large graphs, estimation of similarity distance and person name disambiguation. We argue that two observations are important when finding top-k PPR lists. 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 and the exact values of PPR are by far not so crucial. Secondly, by allowing a small number of “wrong” elements in top-k lists, we achieve great computational savings, in fact, without degrading the quality of the results. Based on these ideas, we propose Monte Carlo methods for quick detection of top-k PPR lists. We demonstrate the effectiveness of these methods on the Web and Wikipedia graphs, provide performance evaluation and supply stopping criteria.
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