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Generalized epidemic process on modular networks.

Kihong Chung1, Yongjoo Baek1, Daniel Kim1

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Summary
This summary is machine-generated.

This study explores how social reinforcement and network modularity affect behavior spread. Stronger reinforcement leads to a discontinuous phase transition, while weaker reinforcement results in a continuous one.

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Area of Science:

  • Complex systems
  • Network science
  • Epidemiology

Background:

  • Behavioral spread in social networks is influenced by social reinforcement and modular structures.
  • Understanding the interplay between these factors is crucial for modeling epidemic dynamics.

Purpose of the Study:

  • To investigate the combined effects of social reinforcement and modularity on generalized epidemic processes.
  • To analyze the phase transition behavior in modular networks with adjustable modularity.

Main Methods:

  • Analytical approach adapted from clique-based random networks.
  • Generalized epidemic process simulation on modular networks.
  • Finite-size scaling analysis and bimodality coefficient analysis for numerical verification.

Main Results:

  • A continuous phase transition (bond-percolation type) is observed under weak social reinforcement.
  • A discontinuous phase transition occurs when social reinforcement is sufficiently strong.
  • Findings were numerically validated, confirming the analytical predictions.

Conclusions:

  • The interplay between social reinforcement and network modularity dictates the nature of behavioral spread transitions.
  • Network structure and reinforcement strength are critical parameters in epidemic modeling.
  • This research provides insights into phase transitions in complex social systems.