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A Sharp PageRank Algorithm with Applications to Edge Ranking and Graph Sparsification

  • Fan Chung
  • Wenbo Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6516)

Abstract

We give an improved algorithm for computing personalized PageRank vectors with tight error bounds which can be as small as Ω(n − p ) for any fixed positive integer p. The improved PageRank algorithm is crucial for computing a quantitative ranking of edges in a given graph. We will use the edge ranking to examine two interrelated problems – graph sparsification and graph partitioning. We can combine the graph sparsification and the partitioning algorithms using PageRank vectors to derive an improved partitioning algorithm.

Keywords

Weighted Graph Network Design Problem Graph Partitioning Sparse Graph Approximation Guarantee 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fan Chung
    • 1
  • Wenbo Zhao
    • 1
  1. 1.University of CaliforniaSan Diego, La JollaUSA

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