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Discrimination reveals reconstructability of multiplex networks from partial observations.

Mincheng Wu1, Jiming Chen1, Shibo He1

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Summary

This study introduces a mathematical framework for reconstructing multiplex networks. Network discrimination, an indicator of layer differences, determines reconstruction accuracy, enabling optimal link allocation strategies.

Keywords:
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Area of Science:

  • Network science
  • Complex systems analysis
  • Information theory

Background:

  • Link prediction in monoplex networks is established, but multiplex network reconstruction lacks robust mathematical tools.
  • Existing methods struggle to reconstruct multiplex network structures from aggregate topology and partial link data.
  • Accurate reconstruction of multiplex networks is crucial for understanding complex systems.

Purpose of the Study:

  • To develop a theoretical and computational framework for reconstructing multiplex networks.
  • To identify key factors influencing the accuracy of multiplex network reconstruction.
  • To propose optimal strategies for allocating observed links across layers to enhance reconstruction.

Main Methods:

  • Developed a probability space with maximum likelihood estimation for structural inference.
  • Introduced 'discrimination' as an entropy-based indicator of inter-layer differences.
  • Utilized empirical data from biological, social, and engineered systems, alongside synthetic networks.

Main Results:

  • The proposed framework enables accurate reconstruction of multiplex networks.
  • Network discrimination quantifies the reconstructability of multiplex networks.
  • Optimal link allocation strategies were designed based on discrimination values.
  • Theoretical findings were validated across diverse real-world and synthetic network datasets.

Conclusions:

  • The developed framework provides a novel mathematical tool for multiplex network reconstruction.
  • Discrimination is a critical determinant of multiplex network reconstructability.
  • The findings facilitate improved strategies for network data acquisition and analysis in multiplex systems.