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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Symbolic regression of generative network models.

Telmo Menezes1, Camille Roth2

  • 11] Centre Marc Bloch Berlin (An-Institut der Humboldt Universität, UMIFRE CNRS-MAE) Friedrichstr. 191, 10117 Berlin, Germany [2] Centre d'Analyse et de Mathématique Sociales (UMR 8557 CNRS-EHESS) 190 av. de France, 75013 Paris, France.

Scientific Reports
|September 6, 2014
PubMed
Summary
This summary is machine-generated.

We developed a general machine learning method to automatically discover realistic network growth models from data. This approach, inspired by natural selection, simplifies understanding complex network structures in science.

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Last Updated: Apr 24, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Area of Science:

  • Network science
  • Computational biology
  • Social network analysis

Background:

  • Networks are crucial abstractions across scientific disciplines, aiding systematic analysis of phenomena.
  • Developing accurate network morphology and growth models is challenging, often requiring non-intuitive insights.
  • A general method for discovering network growth models from empirical data is currently lacking.

Purpose of the Study:

  • To develop a general approach for automatically detecting realistic decentralized network growth models from empirical data.
  • To create a unified formalism for describing network models as computer programs.
  • To validate the approach by rediscovering known growth laws and identifying new ones for real-world networks.

Main Methods:

  • Employed a machine learning technique inspired by natural selection.
  • Defined a unified formalism to represent network growth models as computer programs.
  • Applied the method to diverse canonical network generation models and real-world network data.

Main Results:

  • Successfully rediscovered pre-defined growth laws for canonical network models.
  • Identified credible growth laws for diverse real-world networks, including brain and social networks.
  • Generated simple, understandable programs representing the discovered network growth mechanisms.

Conclusions:

  • The developed machine learning approach provides a general and effective method for discovering network growth models.
  • The formalism allows for the creation of understandable computer programs representing network dynamics.
  • This method facilitates deeper insights into the mechanisms underlying complex network formation across scientific fields.