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Updated: Jul 27, 2025
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Revealing Neural Circuit Topography in Multi-Color
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Topology identification for stochastic multi-layer networks via graph-theoretic method
Chunmei Zhang1, Ran Li1, Quanxin Zhu2
1School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China.
Summary
This study identifies the unknown topology of stochastic multi-layer networks using adaptive control. It also establishes finite-time identification criteria for these complex network structures.
Area of Science:
- Network Science
- Control Theory
- Applied Mathematics
Background:
- Multi-layer networks significantly impact system dynamics, yet their topology is often unknown.
- Stochastic perturbations add complexity to analyzing network behavior.
Purpose of the Study:
- To investigate topology identification for multi-layer networks with stochastic perturbations.
- To develop methods for determining network structure when it is not explicitly known.
Main Methods:
- Utilized graph-theoretic methods and Lyapunov functions for analysis.
- Designed a suitable adaptive controller for topology identification.
- Employed finite-time control techniques to estimate identification time.
Main Results:
- Derived criteria for topology identification in stochastic multi-layer networks.
- Established finite-time identification criteria, estimating the time required.
- Validated theoretical findings through numerical simulations on double-layer Watts-Strogatz networks.
Conclusions:
- The proposed adaptive control approach effectively identifies the topology of stochastic multi-layer networks.
- Finite-time control provides valuable insights into the efficiency of the identification process.
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