We 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. © 2011 Springer-Verlag.
CITATION STYLE
Avrachenkov, K., Litvak, N., Nemirovsky, D., Smirnova, E., & Sokol, M. (2011). Quick detection of top-k personalized PageRank lists. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6732 LNCS, pp. 50–61). https://doi.org/10.1007/978-3-642-21286-4_5
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