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Delusive PageRank in Incomplete Graphs

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Complex Networks and Their Applications VII (COMPLEX NETWORKS 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 812))

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Abstract

Most real-world graphs collected from the Web like Web graphs and social network graphs are incomplete. This leads to inaccurate estimates of graph properties based on link analysis such as PageRank. In this paper we focus on studying such deviations in ordering/ranking imposed by PageRank over incomplete graphs. We first show that deviations in rankings induced by PageRank are indeed possible. We measure how much a ranking, induced by PageRank, on an input graph could deviate from the original unseen graph. More importantly, we are interested in conceiving a measure that approximates the rank correlation among them without any knowledge of the original graph. To this extent we formulate the HAK measure that is based on computing the impact redistribution of PageRank according to the local graph structure. Finally, we perform extensive experiments on both real-world Web and social network graphs with more than 100M vertices and 10B edges as well as synthetic graphs to showcase the utility of HAK.

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Notes

  1. 1.

    The archive has been generously provided to us by the Internet Archive.

  2. 2.

    http://alexandria-project.eu

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Acknowledgements

This work is partially funded by ALEXANDRIA (ERC 339233).

Author information

Authors and Affiliations

  1. L3S Research Center, Leibniz University, Appelstrasse 9a, 30169, Hannover, Germany

    Avishek Anand

  2. KBS, Leibniz University, Appelstrasse 4, 30169, Hannover, Germany

    Helge Holzmann & Megha Khosla

Authors
  1. Helge Holzmann
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  2. Avishek Anand
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  3. Megha Khosla
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Corresponding author

Correspondence to Megha Khosla .

Editor information

Editors and Affiliations

  1. Nokia Bell Labs, Cambridge, UK

    Luca Maria Aiello

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I UMR CNRS 6306 9, University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  4. Mathematical Institute, University of Oxford, Oxford, UK

    Renaud Lambiotte

  5. Department of Computer Science and Technology, The Computer Laboratory, University of Cambridge, Cambridge, UK

    Pietro Lió

  6. Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

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Holzmann, H., Anand, A., Khosla, M. (2019). Delusive PageRank in Incomplete Graphs. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_9

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