Journal Article

Full reconstruction of simplicial complexes from binary contagion and Ising data

Wang, Huan, Chen, Han-Shuang, Lai, Ying-Cheng, Ma, Chuang, Zhang, Hai-Feng

2022

Overview

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. Data-driven recovery of topology is challenging for networks beyond pairwise interactions. The authors propose a framework to reconstruct complex networks with higher-order interactions from time series, focusing on networks with 2-simplexes where social contagion and Ising dynamics generate binary data.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/705/1041 / 639/766/530/2801 / Binary data / Computer applications / Data recovery / Graphs / Humanities and Social Sciences / Inverse problems / Ising model / Many body problem / multidisciplinary / Networks / Reconstruction / Science / Science (multidisciplinary) / Statistical analysis / Statistical inference / Time series / Topology