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Global and partitioned reconstructions of undirected complex networks
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.Comment: 6 pages, 2 figures, 1 table; revised version; added numerical results
of the PR in Table 1 and expanded Section 4; added 7 reference
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