Network reconstruction based on evolutionary-game data via compressive sensing

Wen-Xu Wang*, Ying-Cheng Lai, Celso Grebogi, Jieping Ye

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

90 Citations (Scopus)

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 of complex networks can also be addressed even when the underlying dynamical processes are governed by realistic, evolutionary-game type of interactions in discrete time.

Original languageEnglish
Article number021021
Number of pages7
JournalPhysical Review X
Volume1
Issue number2
DOIs
Publication statusPublished - 21 Dec 2011

Keywords

  • scale-free networks
  • prisoners-dilemma game
  • social dilemmas
  • time-series
  • events
  • dynamics
  • cooperation
  • compressive sensing
  • network reconstruction

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