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Evolutionary games model a common type of interactions in a variety of complex, networked, natural systems and social systems. Given such a system, uncovering the interacting structure of the underlying

Evolutionary games model a common type of interactions in a variety of complex, networked, natural systems and social systems. Given such a system, uncovering the interacting structure of the underlying network is key to understanding its collective dynamics. Based on compressive sensing, we develop an efficient approach to reconstructing complex networks under game-based interactions from small amounts of data. The method is validated by using a variety of model networks and by conducting an actual experiment to reconstruct a social network.

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    Date Created
    • 2011-12-21
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  • Text
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    Identifier
    • Digital object identifier: 10.1103/PhysRevX.1.021021
    • Identifier Type
      International standard serial number
      Identifier Value
      2160-3308
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    Wang, W., Lai, Y., Grebogi, C., & Ye, J. (2011). Network Reconstruction Based on Evolutionary-Game Data via Compressive Sensing. Physical Review X, 1(2). doi:10.1103/physrevx.1.021021

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