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Dynamic Structural Equation Models for Directed Cyclic Graphs: the Structural Identifiability Problem.

Yulin Wang1, Yu Luo2, Hulin Wu3

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.

Statistics and Its Interface
|July 25, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient method to ensure parameter identifiability in dynamic structural equation models (DSEMs) for cyclic networks. The approach uses frequency domain analysis and Mason

Keywords:
00K0000K0100K0200K03Cyclic networkDynamic structural equation modelFeedback loopStructural identifiability analysis

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Area of Science:

  • Network science
  • Statistical modeling
  • Systems dynamics

Background:

  • Network dynamics, the study of collective node state changes over time, is a key research area.
  • Dynamic Structural Equation Models (DSEMs) are increasingly used for network dynamics analysis.
  • Parameter identifiability is crucial for reliable inference in DSEMs.

Purpose of the Study:

  • To propose a general and efficient method for assessing structural parameter identifiability in linear DSEMs for cyclic networks.
  • To address the prerequisite of reliable parameter inference by tackling identifiability issues.

Main Methods:

  • Transforming a DSEM into an equivalent frequency domain representation.
  • Employing Mason's gain formula to handle feedback loops in cyclic networks.
  • Utilizing the identifiability matrix method to determine parameter identifiability.

Main Results:

  • A novel, computationally efficient approach for structural parameter identifiability in linear DSEMs of cyclic networks.
  • The method avoids complex symbolic or numerical computations.
  • Demonstrated applicability to brain, social, and molecular interaction networks.

Conclusions:

  • The proposed method provides a reliable way to assess parameter identifiability in DSEMs for cyclic networks.
  • It offers a computationally efficient and broadly applicable solution.
  • The approach is validated through benchmark network examples.