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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)

S+SSPR 2021: Structural, Syntactic, and Statistical Pattern Recognition pp 174–183Cite as

Efficient Partitioning of Partial Correlation Networks

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 12644)

Abstract

Partial correlation is a popular and principled metric for determining edges between nodes in a graph. However when the goal is to both estimate network connectivity from sample data and subsequently partition the result, methods such as spectral clustering can be applied much more efficiency and at larger scale. We derive a method that can similarly partition partial correlation networks directly from sample data. The method is closely related to spectral clustering, and can be implemented with comparable efficiency. Our results also provide new insight into the success of spectral clustering in many fields, as an approximation to clustering of partial correlation networks.

Keywords

  • Partial correlation
  • Spectral clustering
  • Graphical models
  • Graph partitioning

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  • DOI: 10.1007/978-3-030-73973-7_17
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Author information

Authors and Affiliations

  1. University of New Haven, New Haven, CT, 06516, USA

    Keith Dillon

Authors
  1. Keith Dillon
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Corresponding author

Correspondence to Keith Dillon .

Editor information

Editors and Affiliations

  1. Ca’ Foscari University of Venice, Venice, Italy

    Dr. Andrea Torsello

  2. Queen Mary University of London, London, UK

    Luca Rossi

  3. Università Ca' Foscari Venezia, Venice, Italy

    Prof. Marcello Pelillo

  4. University of Cagliari, Cagliari, Italy

    Dr. Battista Biggio

  5. Deakin University, Burwood, VIC, Australia

    Antonio Robles-Kelly

5 Appendix: Matlab Implementation of Clustering

5 Appendix: Matlab Implementation of Clustering

In this appendix we provide efficient Matlab code for performing partial correlation clustering.

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Dillon, K. (2021). Efficient Partitioning of Partial Correlation Networks. In: Torsello, A., Rossi, L., Pelillo, M., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2021. Lecture Notes in Computer Science(), vol 12644. Springer, Cham. https://doi.org/10.1007/978-3-030-73973-7_17

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  • DOI: https://doi.org/10.1007/978-3-030-73973-7_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73972-0

  • Online ISBN: 978-3-030-73973-7

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