Computing Heat Kernel Pagerank and a Local Clustering Algorithm

@article{Graham2014ComputingHK,
  title={Computing Heat Kernel Pagerank and a Local Clustering Algorithm},
  author={Fan Chung Graham and Olivia Simpson},
  journal={ArXiv},
  year={2014},
  volume={abs/1503.03155}
}
Heat kernel pagerank is a variation of Personalized PageRank given in an exponential formulation. In this work, we present a sublinear time algorithm for approximating the heat kernel pagerank of a graph. The algorithm works by simulating random walks of bounded length and runs in time \(O\big (\frac{\log (\epsilon ^{-1})\log n}{\epsilon ^3\log \log (\epsilon ^{-1})}\big )\), assuming performing a random walk step and sampling from a distribution with bounded support take constant time. 
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