Symbolic Regression on Network Properties

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 Genet...

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Bibliographic Details
Published inGenetic Programming Vol. 10196; pp. 131 - 146
Main Authors Märtens, Marcus, Kuipers, Fernando, Van Mieghem, Piet
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2017
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Cartesian Genetic Programming
Complex networks
Symbolic regression
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Summary: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.
ISBN:9783319556956
3319556959
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-55696-3_9