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  • 1Science and Technology Faculty, Computer Science Department, Universidade Nova de Lisboa, Lisboa, Portugal.

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Summary
This summary is machine-generated.

We introduce a framework to optimize and validate generative models for complex networks, addressing the genotype to phenotype problem. Our method ensures models are non-monotonic for accurate analysis, applicable beyond neuroscience.

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Area of Science:

  • Network science
  • Computational modeling

Background:

  • Generative models analyze complex network growth (genotype to phenotype problem).
  • A complete methodology, especially for model validation, remains an open challenge.

Purpose of the Study:

  • To present a quantitative framework for optimizing and validating generative model creation.
  • To establish criteria for model utility in understanding network topology.

Main Methods:

  • Utilizing a classification task to estimate the precision added by modeled genotype.
  • Evaluating feature selection, parameter optimization, and result validation.

Main Results:

  • A minimum requirement for generative models is a non-monotonic genotype-to-phenotype mapping.
  • A previously published model failed this condition, questioning its use in neurological disorder studies.

Conclusions:

  • The proposed framework offers a robust method for generative model development and validation.
  • This approach is broadly applicable to various network types, including social and technological networks.