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Measuring the Sensitivity of Graph Metrics to Missing Data

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Parallel Processing and Applied Mathematics (PPAM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8384))

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Abstract

The increasing energy consumption of high performance computing has resulted in rising operational and environmental costs. Therefore, reducing the energy consumption of computation is an emerging area of interest. We study the approach of data sampling to reduce the energy costs of sparse graph algorithms. The resulting error levels for several graph metrics are measured to analyze the trade-off between energy consumption reduction and error. The three types of graphs studied, real graphs, synthetic random graphs, and synthetic small-world graphs, each show distinct behavior. Across all graphs, the error cost is initially relatively low. For example, four of the five real graphs studied needed less than a third of total energy to retain a degree centrality rank correlation coefficient of \(0.85\) when random vertices were removed. However, the error incurred for further energy reduction grows at an increasing rate, providing diminishing returns.

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Acknowledgement

The work depicted in this paper was partially sponsored by Defense Advanced Research Projects Agency (DARPA) under agreement #HR0011-13-2-0001. The content, views and conclusions presented in this document do not necessarily reflect the position or the policy of DARPA or the U.S. Government, no official endorsement should be inferred. Distribution Statement A: “Approved for public release; distribution is unlimited.”

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Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, GA, USA

    Anita Zakrzewska & David A. Bader

Authors
  1. Anita Zakrzewska
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  2. David A. Bader
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Corresponding author

Correspondence to Anita Zakrzewska .

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Editors and Affiliations

  1. Institute of Computer and Information Science, Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Tennessee, Department of Computer Science, Knoxville, Tennessee, USA

    Jack Dongarra

  3. Institute of Computer and Information Science, Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

  4. Technical University of Denmark Informatics and Mathematical Modelling, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Zakrzewska, A., Bader, D.A. (2014). Measuring the Sensitivity of Graph Metrics to Missing Data. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2013. Lecture Notes in Computer Science(), vol 8384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55224-3_73

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  • DOI: https://doi.org/10.1007/978-3-642-55224-3_73

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-55223-6

  • Online ISBN: 978-3-642-55224-3

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