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Restricted Network Reconstruction from Time Series via Dempster-Shafer Evidence Theory.
Cai Zhang1, Yishu Xian1, Xiao Yuan1
1School of Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Entropy (Basel, Switzerland)
|February 27, 2026
Summary
This study introduces a new framework for reconstructing complex network topology from limited data by combining epidemic modeling with Dempster-Shafer theory. The method effectively handles uncertainty and achieves high accuracy in network reconstruction across various scales.
Area of Science:
- Complex Systems Science
- Network Science
- Mathematical Modeling
Background:
- Complex networks model interactions in diverse systems.
- Network topology reconstruction is challenging due to unobservable connections and limited data.
- Sparse local observations necessitate robust inference methods.
Purpose of the Study:
- To develop a novel framework for network topology reconstruction under sparse local observations.
- To integrate epidemic dynamics with Dempster-Shafer (DS) evidence theory for improved network inference.
- To address uncertainty and conflict in data for accurate network structure determination.
Main Methods:
- A two-level belief fusion process: intra-node fusion and inter-node fusion.
- Intra-node fusion aggregates SIR simulation results to generate Basic Probability Assignments (BPAs), quantifying uncertainty.
- Inter-node fusion combines BPAs from multiple seed nodes using DS theory for global topology synthesis.
Main Results:
- The proposed framework demonstrates effectiveness and robustness in network reconstruction.
- High and stable reconstruction accuracy was achieved on both synthetic and real-world networks (Zachary's Karate Club).
- The method scales successfully to large-scale networks, attaining an average accuracy of 0.85.
Conclusions:
- The dual-fusion framework effectively handles uncertainty and conflict in sparse, stochastic observations.
- The approach offers practical applicability for network topology reconstruction across different scales and densities.
- This method provides a robust solution for inferring network structures from limited observational data.
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