Multiscale Reconstruction of Multiplex Networks with Higher-Order Interactions
Abstract
Inferring network structure from dynamical observations is a fundamental inverse problem in complex systems. Existing approaches have largely focused on single-layer or pairwise interaction networks, leaving the reconstructability of multiplex systems with higher-order interactions poorly understood. Here, we show that structural identifiability in such systems is jointly governed by cross-layer spreading couplings and network heterogeneity. Building on coupled awareness-behavior dynamics, we develop a multiscale inference framework that disentangles interacting spreading processes and enables the simultaneous reconstruction of microscopic simplicial interactions and macroscopic metapopulation organization. Numerical experiments on synthetic and empirical networks demonstrate high reconstruction performance, particularly in non-absorbing dynamical regimes sustained by weak inhibitory and strong facilitatory couplings. Results reveal the mechanisms governing structural identifiability in higher-order multiplex networks and provide a general route for inverse inference in multiscale complex systems.
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BibTeX
@misc{mei2026multiscale,
title = {{Multiscale Reconstruction of Multiplex Networks with Higher-Order Interactions}},
author = {Guangyuan Mei and Yao Cai and Suoyi Tan and Chuang Liu and Xiu-Xiu Zhan and Xin Lu and Yi-Cheng Zhang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.17354},
eprint = {2609.17354},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.17354},
}
RIS
TY - UNPB
TI - Multiscale Reconstruction of Multiplex Networks with Higher-Order Interactions
AU - Guangyuan Mei
AU - Yao Cai
AU - Suoyi Tan
AU - Chuang Liu
AU - Xiu-Xiu Zhan
AU - Xin Lu
AU - Yi-Cheng Zhang
PY - 2026
DA - 2026/09/15
PB - arXiv
DO - 10.48550/arxiv.2609.17354
UR - https://arxiv.org/abs/2609.17354
AB - Inferring network structure from dynamical observations is a fundamental inverse problem in complex systems. Existing approaches have largely focused on single-layer or pairwise interaction networks, leaving the reconstructability of multiplex systems with higher-order interactions poorly understood. Here, we show that structural identifiability in such systems is jointly governed by cross-layer spreading couplings and network heterogeneity. Building on coupled awareness-behavior dynamics, we develop a multiscale inference framework that disentangles interacting spreading processes and enables the simultaneous reconstruction of microscopic simplicial interactions and macroscopic metapopulation organization. Numerical experiments on synthetic and empirical networks demonstrate high reconstruction performance, particularly in non-absorbing dynamical regimes sustained by weak inhibitory and strong facilitatory couplings. Results reveal the mechanisms governing structural identifiability in higher-order multiplex networks and provide a general route for inverse inference in multiscale complex systems.
ER -