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Published on: October 13, 2023
Information cascades on degree-correlated random networks
Joshua L Payne1, Peter Sheridan Dodds, Margaret J Eppstein
1Department of Computer Science, The University of Vermont, Burlington, Vermont 05405, USA.
Summary
Social contagion cascades spread more widely on networks with positive degree-degree correlations. Paradoxically, large cascades can occur with very positive or very negative correlations, but not weak ones, impacting information spread.
Area of Science:
- Network science
- Sociophysics
- Computational social science
Background:
- Understanding information cascades is crucial in social networks.
- Degree-degree correlations influence network structure and dynamics.
- Previous models often simplify network correlations.
Purpose of the Study:
- To investigate the impact of degree-degree correlations on social contagion.
- To analyze the conditions for global information cascades in threshold models.
- To explore how network structure affects information spread.
Main Methods:
- Numerical simulations of a threshold model.
- Analysis of degree-correlated random networks.
- Examination of information cascade dynamics.
Main Results:
- Positive degree-degree correlations generally expand the conditions for global cascades.
- Large cascades can paradoxically occur with strong positive or negative correlations.
- Weak correlations may inhibit large-scale information cascades.
- Initial node degree's influence on cascade size is significantly modulated by correlations.
Conclusions:
- Degree-degree correlations play a complex role in social contagion.
- Network topology, specifically correlations, can lead to non-intuitive cascade behavior.
- Findings have implications for information dissemination and network design.
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