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Detecting Sharp Drops in PageRank and a Simplified Local Partitioning Algorithm

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 4484)

Abstract

We show that whenever there is a sharp drop in the numerical rank defined by a personalized PageRank vector, the location of the drop reveals a cut with small conductance. We then show that for any cut in the graph, and for many starting vertices within that cut, an approximate personalized PageRank vector will have a sharp drop sufficient to produce a cut with conductance nearly as small as the original cut. Using this technique, we produce a nearly linear time local partitioning algorithm whose analysis is simpler than previous algorithms.

Keywords

  • Sharp Drop
  • Rank Function
  • Small Conductance
  • Approximation Guarantee
  • Local Partitioning

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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References

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© 2007 Springer-Verlag Berlin Heidelberg

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Andersen, R., Chung, F. (2007). Detecting Sharp Drops in PageRank and a Simplified Local Partitioning Algorithm. In: Cai, JY., Cooper, S.B., Zhu, H. (eds) Theory and Applications of Models of Computation. TAMC 2007. Lecture Notes in Computer Science, vol 4484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72504-6_1

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  • DOI: https://doi.org/10.1007/978-3-540-72504-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72503-9

  • Online ISBN: 978-3-540-72504-6

  • eBook Packages: Computer ScienceComputer Science (R0)