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Growth-induced percolation on complex networks.

Hongliang Sun1,2, Shuhuan Chen3, Jiarong Xie4,5

  • 1College of Wealth Management, Ningbo University of Finance and Economics, No.899 Xueyuan Road, Haishu District, Ningbo, Zhejiang 315175, China.

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|June 27, 2025
PubMed
Summary
This summary is machine-generated.

Indirect social interactions significantly influence behaviors and trigger high-impact research. A new model, growth-induced percolation, explains this activation through indirect connections, revealing distinct phase transitions.

Keywords:
complex networksindirect influencespercolationphase transitionsocial networks

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Area of Science:

  • Social network analysis
  • Complex systems science
  • Behavioral economics

Background:

  • Empirical studies increasingly recognize indirect social interactions' impact on human behavior.
  • Theoretical models have predominantly focused on direct social influences, neglecting indirect pathways.
  • Understanding how behaviors spread through indirect connections is crucial for various social systems.

Purpose of the Study:

  • To investigate the role of both direct and indirect social interactions in triggering high-impact research periods.
  • To propose a novel theoretical model, growth-induced percolation, explaining individual activation via indirect interactions.
  • To analyze the phase transition behaviors and hysteresis in the proposed model.

Main Methods:

  • Analysis of scientific collaboration networks to identify patterns of direct and indirect influence.
  • Development of the growth-induced percolation model to simulate the spread of activation through indirect interactions.
  • Examination of the model's phase transition dynamics and hysteresis loop asymmetry.

Main Results:

  • Direct and indirect collaborators were identified as key factors in initiating high-impact research.
  • The growth-induced percolation model successfully captures individual activation driven by indirect interactions.
  • A significant asymmetry was observed in the hysteresis loop of the model, indicating distinct forward and reverse process behaviors.

Conclusions:

  • Indirect social interactions play a critical role in shaping behaviors and driving significant outcomes, such as high-impact research.
  • The growth-induced percolation model offers a foundational framework for understanding behavior propagation in social systems.
  • Findings have broad implications for fields including scientific collaboration, social contagion, and network dynamics.