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A non-parametric framework for learning network growth models

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GrowCode

A non-parametric framework for learning how networks grow

This tool accompanies the paper:

R. Patro+ G. Duggal+, E. Sefer, H. Wang, D. Filippova, C. Kingsford. The missing models: a data driven approach for learning how networks grow. To Appear: Proc. 18th Intl. Conference on Knowledge Discovery and Data Mining (KDD) 2012.

+ Equal contributions

Requirements

For building and editing source:

Running

The 'runexample.sh' script shows how to run a basic learning and program selection procedure using the SBT framework. The learning procedure is associated with a variety of genetic algorithm parameters that can be customized in each example's '.params' file. Please see the ECJ documentation (ECJ link) for more information on what these parameters mean.

To Do

  • Make the network properties tested in BestProgramProblem.scala selectable via a config file or command line interface
  • Add a runnable jar to the repo

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A non-parametric framework for learning network growth models

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