Global and partitioned reconstructions of undirected complex networks

Ming Xu, Chuan-Yun Xu, Huan Wang, Yong-Kui Li, Jing-Bo Hu and Ke-Fei Cao ()
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Ming Xu: Center for Nonlinear Complex Systems, Department of Physics, School of Physics and Astronomy, Yunnan University
Chuan-Yun Xu: Center for Nonlinear Complex Systems, Department of Physics, School of Physics and Astronomy, Yunnan University
Huan Wang: School of Computer Science and Technology, Baoji University of Arts and Sciences
Yong-Kui Li: Center for Nonlinear Complex Systems, Department of Physics, School of Physics and Astronomy, Yunnan University
Jing-Bo Hu: Center for Nonlinear Complex Systems, Department of Physics, School of Physics and Astronomy, Yunnan University
Ke-Fei Cao: Center for Nonlinear Complex Systems, Department of Physics, School of Physics and Astronomy, Yunnan University

The European Physical Journal B: Condensed Matter and Complex Systems, 2016, vol. 89, issue 3, 1-6

Abstract: Abstract It is a significant challenge to predict the network topology from a small amount of dynamical observations. Different from the usual framework of the node-based reconstruction, two optimization approaches (i.e., the global and partitioned reconstructions) are proposed to infer the structure of undirected networks from dynamics. These approaches are applied to evolutionary games occurring on both homogeneous and heterogeneous networks via compressed sensing, which can more efficiently achieve higher reconstruction accuracy with relatively small amounts of data. Our approaches provide different perspectives on effectively reconstructing complex networks.

Keywords: Statistical; and; Nonlinear; Physics (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1140/epjb/e2016-60956-2

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