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WWW '05 Proceedings of the 14th international conference on World Wide Web
Scaling link-based similarity search
WWW '05 Proceedings of the 14th international conference on World Wide Web
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Foundations and Trends® in Theoretical Computer Science
Efficiency-quality tradeoffs for vector score aggregation
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Personalized query expansion for the web
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
Spectral Clustering in Social Networks
Advances in Web Mining and Web Usage Analysis
Mining graph patterns efficiently via randomized summaries
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WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
Local partitioning for directed graphs using PageRank
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Fast nearest-neighbor search in disk-resident graphs
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Fast incremental and personalized PageRank
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Semi-supervised ranking on very large graphs with rich metadata
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Multiplicative approximations of random walk transition probabilities
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Search Computing
On the embeddability of random walk distances
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Personalized PageRank expresses link-based page quality around user selected pages. The only previous personalized PageRank algorithm that can serve on-line queries for an unrestricted choice of pages on large graphs is our Monte Carlo algorithm [WAW 2004]. In this paper we achieve unrestricted personalization by combining rounding and randomized sketching techniques in the dynamic programming algorithm of Jeh and Widom [WWW 2003]. We evaluate the precision of approximation experimentally on large scale real-world data and find significant improvement over previous results. As a key theoretical contribution we show that our algorithms use an optimal amount of space by also improving earlier asymptotic worst-case lower bounds. Our lower bounds and algorithms apply to the SimRank as well; of independent interest is the reduction of the SimRank computation to personalized PageRank.