• Media type: E-Article; Electronic Conference Proceeding; Text
  • Title: Density Independent Algorithms for Sparsifying k-Step Random Walks
  • Contributor: Jindal, Gorav [Author]; Kolev, Pavel [Author]; Peng, Richard [Author]; Sawlani, Saurabh [Author]
  • imprint: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2017
  • Language: English
  • DOI: https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2017.14
  • Keywords: effective resistances ; graph sparsification ; spectral graph theory ; random walks
  • Origination:
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  • Description: We give faster algorithms for producing sparse approximations of the transition matrices of k-step random walks on undirected and weighted graphs. These transition matrices also form graphs, and arise as intermediate objects in a variety of graph algorithms. Our improvements are based on a better understanding of processes that sample such walks, as well as tighter bounds on key weights underlying these sampling processes. On a graph with n vertices and m edges, our algorithm produces a graph with about nlog(n) edges that approximates the k-step random walk graph in about m + k^2 nlog^4(n) time. In order to obtain this runtime bound, we also revisit "density independent" algorithms for sparsifying graphs whose runtime overhead is expressed only in terms of the number of vertices.
  • Access State: Open Access