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Symbolic Regression on Network Properties

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Genetic Programming (EuroGP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10196))

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Abstract

Networks are continuously growing in complexity, which creates challenges for determining their most important characteristics. While analytical bounds are often too conservative, the computational effort of algorithmic approaches does not scale well with network size. This work uses Cartesian Genetic Programming for symbolic regression to evolve mathematical equations that relate network properties directly to the eigenvalues of network adjacency and Laplacian matrices. In particular, we show that these eigenvalues are powerful features to evolve approximate equations for the network diameter and the isoperimetric number, which are hard to compute algorithmically. Our experiments indicate a good performance of the evolved equations for several real-world networks and we demonstrate how the generalization power can be influenced by the selection of training networks and feature sets.

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Notes

  1. 1.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, PO Box 5031, 2600 GA, Delft, The Netherlands

    Marcus Märtens, Fernando Kuipers & Piet Van Mieghem

Authors
  1. Marcus Märtens
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  2. Fernando Kuipers
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  3. Piet Van Mieghem
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Corresponding author

Correspondence to Marcus Märtens .

Editor information

Editors and Affiliations

  1. University College Dublin , Dublin, Ireland

    James McDermott

  2. Universidade Nova de Lisboa , Lisbon, Portugal

    Mauro Castelli

  3. Brno University of Technology , Brno, Czech Republic

    Lukas Sekanina

  4. Vrije Universiteit Amsterdam , Amsterdam, The Netherlands

    Evert Haasdijk

  5. University of Cádiz , Cádiz, Spain

    Pablo García-Sánchez

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Märtens, M., Kuipers, F., Van Mieghem, P. (2017). Symbolic Regression on Network Properties. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2017. Lecture Notes in Computer Science(), vol 10196. Springer, Cham. https://doi.org/10.1007/978-3-319-55696-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-55696-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55695-6

  • Online ISBN: 978-3-319-55696-3

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