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Quantifying dynamical spillover in co-evolving multiplex networks.

Vikram S Vijayaraghavan1,2, Pierre-André Noël1,3, Zeev Maoz4,5

  • 1Complexity Sciences Center, University of California, Davis CA 95616, USA.

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|October 14, 2015
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Summary
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Researchers developed a Multiplex Markov chain to identify correlations in multiplex networks. This method reveals "dynamical spillover," where changes in one network layer influence another, offering insights into causal pathways.

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Area of Science:

  • Network Science
  • Complex Systems
  • Data Analysis

Background:

  • Multiplex networks, systems with multiple link types sharing common nodes, are prevalent across various fields.
  • Correlated edge dynamics in these networks significantly impact system-wide processes.
  • Extracting these correlations from real-world longitudinal data remains a significant challenge.

Purpose of the Study:

  • To introduce a novel method for quantifying correlations in edge dynamics within multiplex networks.
  • To differentiate genuine correlations from random simultaneous changes in network layers.
  • To analyze real-world multiplex network data to uncover inter-layer dynamics.

Main Methods:

  • Development of the Multiplex Markov chain for analyzing longitudinal multiplex network data.
  • Comparison of multiplex network analysis results against a null model assuming independent layers.
  • Application of the method to trade/alliance networks and open-source software development networks.

Main Results:

  • Quantification of correlations in edge dynamics across different layers of multiplex networks.
  • Identification of "dynamical spillover," where the formation or deletion of edges in one layer is correlated with changes in other layers.
  • Demonstration of correlated dynamics in both nation-state and open-source software development networks.

Conclusions:

  • The Multiplex Markov chain effectively quantifies inter-layer correlations in multiplex networks.
  • "Dynamical spillover" is a demonstrable phenomenon in real-world multiplex systems.
  • Analysis of temporal network dynamics provides insights into potential causal relationships between network layers.