Multiscale Matrix Sampling and Sublinear-Time PageRank Computation

  title={Multiscale Matrix Sampling and Sublinear-Time PageRank Computation},
  author={Christian Borgs and Mickey Brautbar and Jennifer T. Chayes and Shang-Hua Teng},
  journal={Internet Mathematics},
A fundamental problem arising in many applications in Web science and social network analysis is the problem of identifying all nodes in a network whose PageRank exceeds a given threshold ∆. In this paper, we study the probabilistic version of the problem where given an arbitrary approximation factor c > 1, we are asked to output a set S of nodes such that with high probability, S contains all nodes of PageRank at least ∆, and no node of PageRank smaller than ∆/c. We call this problem… CONTINUE READING
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