Full reconstruction of simplicial complexes from binary contagion and Ising data

Huan Wang, Chuang Ma, Han-Shuang Chen, Ying-Cheng Lai and Hai-Feng Zhang ()
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Huan Wang: Anhui University
Chuang Ma: Anhui University
Han-Shuang Chen: Anhui University
Ying-Cheng Lai: Arizona State University
Hai-Feng Zhang: Anhui University

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract Previous efforts on data-based reconstruction focused on complex networks with pairwise or two-body interactions. There is a growing interest in networks with higher-order or many-body interactions, raising the need to reconstruct such networks based on observational data. We develop a general framework combining statistical inference and expectation maximization to fully reconstruct 2-simplicial complexes with two- and three-body interactions based on binary time-series data from two types of discrete-state dynamics. We further articulate a two-step scheme to improve the reconstruction accuracy while significantly reducing the computational load. Through synthetic and real-world 2-simplicial complexes, we validate the framework by showing that all the connections can be faithfully identified and the full topology of the 2-simplicial complexes can be inferred. The effects of noisy data or stochastic disturbance are studied, demonstrating the robustness of the proposed framework.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30706-9

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DOI: 10.1038/s41467-022-30706-9

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