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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Published on: August 21, 2019

Coevolution and correlated multiplexity in multiplex networks.

Jung Yeol Kim1, K-I Goh

  • 1Department of Physics, Korea University, Seoul 136-713, Korea.

Physical Review Letters
|August 20, 2013
PubMed
Summary
This summary is machine-generated.

We developed a network model to understand how coevolving layers shape complex systems. This coevolution creates correlations across layers, altering system dynamics and suppressing social cascades.

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Area of Science:

  • Network science
  • Complex systems analysis
  • Mathematical modeling

Background:

  • Complex systems often feature multiple interacting layers.
  • Understanding the interplay and coevolution of these layers is crucial for system function.
  • Existing models may not fully capture the dynamics of coevolving network layers.

Purpose of the Study:

  • To propose a modeling framework for understanding multiplex systems based on coevolving network layers.
  • To investigate how the entangled growth of coevolving layers influences network structure.
  • To analyze the impact of coevolution-induced correlated multiplexity on system dynamics.

Main Methods:

  • Developed a modeling framework for coevolving network layers.
  • Utilized minimalistic growing network models as examples.
  • Employed analytical and numerical methods to examine network structure and dynamics.
  • Studied the system's response to a social cascade process.

Main Results:

  • Coevolution of network layers induces strong degree correlations across layers.
  • Coevolution modulates the degree distributions of the network.
  • Correlated multiplexity arising from coevolution alters system dynamics.
  • Demonstrated suppressed susceptibility to social cascade processes due to coevolution.

Conclusions:

  • The proposed coevolutionary framework provides insights into multiplex network structure.
  • Coevolutionary dynamics significantly shape network properties and inter-layer correlations.
  • These structural changes impact system-level dynamical processes, such as cascade suppression.