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Efficient Partitioning of Partial Correlation Networks

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


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.


  • 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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Correspondence to Keith Dillon .

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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.

figure b

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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.

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