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Related Concept Videos

Deactivation Processes: Jablonski Diagram01:25

Deactivation Processes: Jablonski Diagram

Luminescence, the emission of light by a substance that has absorbed energy, is a process that involves the interaction of molecules with light. The energy-level diagram, or Jablonski diagram, is a graphical representation of these interactions, illustrating the various states and transitions a molecule can undergo. In a typical Jablonski diagram, the lowest horizontal line represents the ground-state energy of the molecule, which is usually a singlet state. This state represents the energies...
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Transition State Theory

Transition-state theory, also known as activated-complex theory, provides a molecular-level explanation of reaction rates in both gas-phase and solution-phase reactions. It extends earlier kinetic models by considering the formation of a short-lived, high-energy configuration during a reaction.The progress of a chemical reaction can be represented using a reaction profile, which plots potential energy against the reaction coordinate. As two reactant molecules approach one another, their...
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Related Experiment Video

Updated: Jun 14, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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

Published on: December 7, 2021

Rank-dependent deactivation in network evolution.

Xin-Jian Xu1, Ming-Chen Zhou

  • 1Department of Mathematics, College of Science, Shanghai University, Shanghai, China. xinjxu@shu.edu.cn

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|April 7, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel rank-dependent deactivation mechanism for network evolution models. This approach successfully replicates key real-world network characteristics, including power-law distributions and disassortative correlations.

Related Experiment Videos

Last Updated: Jun 14, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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

Published on: December 7, 2021

Area of Science:

  • Network Science
  • Complex Systems
  • Statistical Physics

Background:

  • Understanding the fundamental mechanisms driving the evolution of complex networks is crucial.
  • Existing models often struggle to capture the emergent properties observed in real-world networks.

Purpose of the Study:

  • To introduce and analyze a novel rank-dependent deactivation mechanism for network evolution.
  • To investigate whether this mechanism can reproduce key topological features of real-world networks.

Main Methods:

  • A network evolution model incorporating a finite memory component.
  • Implementation of a deactivation process where one site is removed at each time step.
  • Analysis of emergent network properties such as degree distribution, clustering coefficient, and degree correlation.

Main Results:

  • The model exhibits a power-law degree distribution, a common feature in many real-world networks.
  • High clustering coefficients were observed, indicating the formation of tightly-knit communities.
  • The model demonstrates disassortative degree correlation, where high-degree nodes tend to connect to low-degree nodes.

Conclusions:

  • The proposed rank-dependent deactivation mechanism is effective in generating realistic network structures.
  • This model provides a parsimonious explanation for the emergence of complex network topologies.
  • The findings contribute to a deeper understanding of network formation and evolution dynamics.