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Synergistic effects in threshold models on networks.

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  • 1Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, Copenhagen 2100-DK, Denmark.

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Synergy in social networks influences meme spreading. A new model shows how neighbor interactions, modeled by a synergy parameter, determine if nodes become active, impacting information diffusion across networks.

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Area of Science:

  • Network Science
  • Computational Social Science
  • Mathematical Modeling

Background:

  • Network structure significantly influences the spread of information, diseases, and behaviors.
  • Understanding social influence dynamics requires tractable models of spreading processes on networks.
  • Existing models may not fully capture complex social behaviors like synergistic adoption.

Purpose of the Study:

  • To incorporate the concept of synergy into a threshold model of social influence on networks.
  • To investigate how synergistic interactions affect the dynamics of meme spreading.
  • To determine the conditions under which nodes with specific degrees become activated.

Main Methods:

  • Developed a deterministic, two-state threshold model incorporating a synergy parameter.
  • The model quantifies how the influence of active neighbors is enhanced or inhibited based on their count.
  • Simulated synergistic meme spreading on random graphs and empirical network data.
  • Employed a heterogeneous mean-field approximation for theoretical analysis.

Main Results:

  • The synergy parameter critically influences system dynamics, dictating the activation possibility of degree-k nodes.
  • Synergistic effects can lead to accelerated adoption or saturation, mimicking real-world social behavior.
  • The heterogeneous mean-field approximation accurately predicts activation thresholds for various network types.

Conclusions:

  • Synergy is a crucial factor in social influence models, significantly altering spreading dynamics.
  • The developed model provides a framework for understanding how network structure and local interactions govern information diffusion.
  • Theoretical analysis and simulations confirm the impact of synergy on node activation and network-level spreading patterns.