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.
Original language | English (US) |
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Article number | 3043 |
Journal | Nature communications |
Volume | 13 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2022 |
ASJC Scopus subject areas
- General Physics and Astronomy
- General Chemistry
- General Biochemistry, Genetics and Molecular Biology