Reconstructing multiplex networks with higher-order interactions from dynamics

Weifang Huang, Ying Xie, Zhiqiu Ye, Xuening Li, Ya Jia and Xuan Zhan

Chaos, Solitons & Fractals, 2026, vol. 208, issue P1

Abstract: Multiplex networks provide an important framework for characterizing multilevel interactions in complex systems, yet reconstructing their structures from dynamical data remains a challenging task. In this work, we propose a unified regression-based framework for multiplex network reconstruction grounded in nodal dynamics and coupling functions. By extending the node-wise regression reconstruction of Malizia et al. (2024) from single-layer systems to multiplex settings, the framework can simultaneously reconstruct intra-layer pairwise interactions, higher-order interaction structures, and one-to-one inter-layer couplings from observed time-series data. Numerical experiments based on the Hindmarsh–Rose neuronal model demonstrate that the proposed method achieves reliable reconstruction performance for directed weighted, undirected unweighted, and higher-order multiplex networks. Furthermore, applications to the Padgett–Florentine families multiplex social network confirm the robustness and generalization capability of the framework under real-world network topologies. Overall, this study provides a general and robust approach for systematically reconstructing multiplex networks and their higher-order interaction structures from time-series data.

Keywords: Multiplex networks; Network structure reconstruction; Higher-order interactions; Dynamical systems (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:208:y:2026:i:p1:s0960077926002614

DOI: 10.1016/j.chaos.2026.118120

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