Inferring structure and dynamics of multiplex networks from single-type observations
Chuang Ma1, Jie Fu1, Hai-Feng Zhang2
1Anhui University, School of Internet, Hefei 230601, China.
Physical Review. E
|January 21, 2026
Summary
This study introduces a new framework for reconstructing multiplex networks, which represent complex systems with multiple interaction types. The method accurately reconstructs network structure and dynamics from limited data, advancing complex systems analysis.
Area of Science:
- Complex Systems Science
- Network Science
- Statistical Physics
Background:
- Multiplex networks model systems with diverse interactions across various scientific domains.
- Reconstructing network topology from limited, aggregated nodal data is challenging due to high dimensionality.
- Existing methods struggle with inferring layer-specific structures and dynamics simultaneously.
Purpose of the Study:
- To develop a robust framework for reconstructing multiplex network topology and dynamics from aggregated nodal data.
- To address the challenge of inferring numerous structural variables from limited observations.
- To provide a theoretically grounded and practically applicable solution for complex system analysis.
Main Methods:
- Proposed a mean-field maximum-likelihood estimation framework to simplify the reconstruction problem.
- Reduced the nonlinear, high-dimensional problem to a tractable system of linear equations.
- Developed an adaptive alternating iteration algorithm for joint reconstruction of structure and dynamics.
Main Results:
- The framework successfully couples network structure with dynamical parameters under homogeneous dynamics assumption.
- Demonstrated superior reconstruction accuracy on synthetic and real-world social networks.
- Exhibited robust noise tolerance and broad applicability to different dynamical models.
Conclusions:
- The proposed framework offers a universal, stable, and theoretically sound method for multiplex network reconstruction.
- This work lays a foundation for improved modeling and analysis of complex systems.
- Enables better understanding and prediction of system dynamics in multiplex network settings.
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