Proceedings of the 11th international conference on World Wide Web
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
The webgraph framework I: compression techniques
Proceedings of the 13th international conference on World Wide Web
Ranking flows from sampled traffic
CoNEXT '05 Proceedings of the 2005 ACM conference on Emerging network experiment and technology
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
Local Graph Partitioning using PageRank Vectors
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Dynamic personalized pagerank in entity-relation graphs
Proceedings of the 16th international conference on World Wide Web
Monte Carlo Methods in PageRank Computation: When One Iteration is Sufficient
SIAM Journal on Numerical Analysis
Pagerank based clustering of hypertext document collections
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Fast incremental and personalized PageRank
Proceedings of the VLDB Endowment
Fast and exact top-k search for random walk with restart
Proceedings of the VLDB Endowment
Efficient personalized pagerank with accuracy assurance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Quick detection of nodes with large degrees
WAW'12 Proceedings of the 9th international conference on Algorithms and Models for the Web Graph
Efficient ad-hoc search for personalized PageRank
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A proximity-based fallback model for hybrid web recommender systems
Proceedings of the 22nd international conference on World Wide Web companion
LR-PPR: locality-sensitive, re-use promoting, approximate personalized pagerank computation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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.