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Network Reconstruction Based on Evolutionary-Game Data via Compressive Sensing

Abstract 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. While most existing methods in this area assume oscillator networks that generate continuous-time data, our work successfully demonstrates that the extremely challenging problem of reverse engineering ... (more)
Created Date 2011-12-21
Contributor Wang, Wen-Xu (ASU author) / Lai, Ying-Cheng (ASU author) / Grebogi, Celso (Author) / Ye, Jieping (ASU author) / Ira A. Fulton Schools of Engineering / School of Electrical, Computer and Energy Engineering / School of Computing, Informatics and Decision Systems Engineering
Type Text
Extent 7 pages
Language English
Identifier DOI: 10.1103/PhysRevX.1.021021 / ISSN: 2160-3308
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Citation 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
Note The final version of this article, as published in Physical Review X, can be viewed online at:
Collaborating Institutions ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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