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Database theory and database practice are typically the domain of computer scientists who adopt what may be termed an algorithmic perspective on their data.
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The geometry of graphs and some of its algorithmic applications
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Complexity and real computation: A manifesto
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Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms
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The PageRank citation ranking: Bringing order to the web
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Additive logistic regression: a statistical view of boosting
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The Elements of Statistical Learning
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MapReduce: Simplified data processing on large clusters
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A survey on PageRank computing
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R. Andersen, F.R.K. Chung, and K. Lang · 2006
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Regularization in statistics
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J. Cohen, B. Dolan, M. Dunlap, J. M. Hellerstein, and C. Welton · 2009
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M. W. Mahoney, L. Orecchia, and N. K. Vishnoi · 2009
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Fast incremental and personalized pagerank
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Large-scale machine learning with stochastic gradient descent
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The heat kernel as the pagerank of a graph
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An algorithm for improving graph partitions
R. Andersen and K. Lang · 2008
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Geometry, flows, and graph-partitioning algorithms
S. Arora, S. Rao, and U. Vazirani · 2008
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The Claremont report on database research
R. Agrawal et al · 2008
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Empirical comparison of algorithms for network community detection
J. Leskovec, K.J. Lang, and M.W. Mahoney · 2010
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Fast personalized PageRank on MapReduce
B. Bahmani, K. Chakrabarti, and D. Xin · 2011
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The push algorithm for spectral ranking
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Randomized algorithms for matrices and data
M. W. Mahoney · 2011
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Implementing regularization implicitly via approximate eigenvector computation
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Regularized Laplacian estimation and fast eigenvector approximation
P. O. Perry and M. W. Mahoney · 2011
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Unpublished results, 2012
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Algorithmic and statistical perspectives on large-scale data analysis
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