@article{4aa99511945449138bac9860563d32c8,
title = "Data based reconstruction of duplex networks",
abstract = "It has been recognized that many complex dynamical systems in the real world require a description in terms of multiplex networks, where a set of common, mutually connected nodes belong to distinct network layers and play a different role in each layer. In spite of recent progress toward data based inference of single-layer networks, to reconstruct complex systems with a multiplex structure remains largely open. In this paper, we articulate a mean-field based maximum likelihood estimation framework to address this problem. In a concrete manner, we reconstruct a class of prototypical duplex network systems hosting two categories of spreading dynamics, and we show that the structures of both layers can be simultaneously reconstructed from time series data. In addition to validating the framework using empirical and synthetic duplex networks, we carry out a detailed analysis to elucidate the impacts of network and dynamics parameters on the reconstruction accuracy and the robustness.",
keywords = "Maximum likelihood estimation, Mean-field approximation, Multiplex networks, Network reconstruction",
author = "Chuang Ma and Chen, {Han Shuang} and Xiang Li and Lai, {Ying Cheng} and Zhang, {Hai Feng}",
note = "Funding Information: This work was supported by NSFC under grant 61973001, 11875069, and 61473001. The work of the third author was supported by the National Science Fund for Distinguished Young Scholars of China (61425019) and NSFC under grant 71731004. The work of the fourth author was supported by ONR through grant N00014-16-1-2828. Funding Information: \ast Received by the editors April 3, 2019; accepted for publication (in revised form) by I. Belykh October 25, 2019; published electronically January 7, 2020. https://doi.org/10.1137/19M1254040 Funding: This work was supported by NSFC under grant 61973001, 11875069, and 61473001. The work of the third author was supported by the National Science Fund for Distinguished Young Scholars of China (61425019) and NSFC under grant 71731004. The work of the fourth author was supported by ONR through grant N00014-16-1-2828. \dagger School of Internet, Anhui University, Hefei 230601, China (chuang m@126.com). \ddagger School of Physics and Material Science, Anhui University, Hefei 230601, China (chenhshf@ahu.edu.cn). \S Adaptive Networks and Control Laboratory, Department of Electronic Engineering, and Center of Smart Networks and Systems, School of Information Science and Engineering, Fudan University, Shanghai 200433, China (lix@fudan.edu.cn). \P School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287 (Ying-Cheng.Lai@asu.edu). \| Corresponding author. School of Mathematical Science, Anhui University, Hefei 230601, China (haifengzhang1978@gmail.com). Publisher Copyright: {\textcopyright} 2020 Society for Industrial and Applied Mathematics Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2020",
doi = "10.1137/19M1254040",
language = "English (US)",
volume = "19",
pages = "124--150",
journal = "SIAM Journal on Applied Dynamical Systems",
issn = "1536-0040",
publisher = "Society of Industrial and Applied Mathematics",
number = "1",
}