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International Workshop on Algorithms and Models for the Web-Graph

WAW 2012: Algorithms and Models for the Web Graph pp 66–77Cite as

Ranking and Sparsifying a Connection Graph

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7323))

Abstract

Many problems arising in dealing with high-dimensional data sets involve connection graphs in which each edge is associated with both an edge weight and a d-dimensional linear transformation. We consider vectorized versions of the PageRank and effective resistance which can be used as basic tools for organizing and analyzing complex data sets. For example, the generalized PageRank and effective resistance can be utilized to derive and modify diffusion distances for vector diffusion maps in data and image processing. Furthermore, the edge ranking of the connection graphs determined by the vectorized PageRank and effective resistance are an essential part of sparsification algorithms which simplify and preserve the global structure of connection graphs.

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of California, San Diego, La Jolla, CA, 92093, USA

    Fan Chung

  2. Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, 92093, USA

    Fan Chung & Wenbo Zhao

Authors
  1. Fan Chung
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  2. Wenbo Zhao
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Ryerson University, M5B 2K3, Toronto, ON, Canada

    Anthony Bonato

  2. Department of Mathematics and Statistics, Dalhousie University, B3H 3J5, Halifax, NS, Canada

    Jeannette Janssen

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© 2012 Springer-Verlag Berlin Heidelberg

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Chung, F., Zhao, W. (2012). Ranking and Sparsifying a Connection Graph. In: Bonato, A., Janssen, J. (eds) Algorithms and Models for the Web Graph. WAW 2012. Lecture Notes in Computer Science, vol 7323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30541-2_6

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  • DOI: https://doi.org/10.1007/978-3-642-30541-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30540-5

  • Online ISBN: 978-3-642-30541-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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