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Compressive sensing-based topology identification of multilayer networks
Guangjun Li1, Na Li2, Suhui Liu3
1College of Sports Engineering and Information Technology, Wuhan Sports University, Hubei 430079, China.
Chaos (Woodbury, N.Y.)
|June 4, 2019
Summary
This study introduces a novel method for identifying multilayer network structures using compressive sensing. The technique effectively reconstructs network topologies from limited data, even with unobservable nodes.
Area of Science:
- Complex networks
- Network science
- Systems biology
Background:
- Understanding network topology is crucial for complex systems.
- Multilayer networks offer a more realistic model for many systems.
- Identifying network structures is challenging, especially in multilayer settings.
Purpose of the Study:
- To propose a novel method for recovering multilayer network topologies.
- To identify both intralayer and interlayer structures simultaneously.
- To enable topology identification even with unobservable nodes.
Main Methods:
- Compressive sensing
- Taylor expansion
- System identification
Main Results:
- Successfully identified multilayer network topologies from limited observations.
- Demonstrated simultaneous recovery of intralayer and interlayer structures.
- Showcased effectiveness even when nodes in one layer are unobservable.
Conclusions:
- The proposed method is effective and efficient for multilayer network topology identification.
- This approach advances the study of complex networks by enabling robust structure recovery.
- The technique has broad applicability in various scientific domains modeling complex systems.
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