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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Analysis of complex contagions in random multiplex networks.

Osman Yağan1, Virgil Gligor

  • 1ECE Department and CyLab, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 4, 2012
PubMed
Summary

We introduce a new model for influence diffusion in multiplex networks, considering how content type affects spread across different link types. This reveals a unique relationship between network structure and global cascade events.

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Area of Science:

  • Network Science
  • Computational Social Science
  • Statistical Physics

Background:

  • Complex contagions spread through social networks, often modeled with linear threshold dynamics.
  • Multiplex networks, with multiple layers of connections, present a more realistic but complex structure for studying diffusion.
  • Existing models often assume homogeneous link properties or single-layer networks, limiting their applicability.

Purpose of the Study:

  • To propose and analyze a novel linear threshold model for influence diffusion in random multiplex networks.
  • To incorporate content-dependent link biases into contagion dynamics.
  • To investigate the conditions, probability, and size of global spreading events in such networks.

Main Methods:

  • Development of a generalized linear threshold model incorporating content-specific link parameters (ci).
  • Mathematical analysis to derive conditions for global spreading events.
  • Calculation of the probability and expected size of global cascades.

Main Results:

  • Derived the condition for global spreading in multiplex networks with content-dependent link weights.
  • Obtained analytical expressions for the probability and expected size of global contagion events.
  • Demonstrated that content significantly impacts diffusion dynamics and network vulnerability.

Conclusions:

  • The proposed model offers a more nuanced understanding of influence diffusion in complex, multilayered systems.
  • Content-specific link properties are crucial for accurately modeling real-world phenomena like rumor or product propagation.
  • A novel connection is established between the giant vulnerable component and global cascade conditions in multiplex networks.