Deep-learning reconstruction of complex dynamical networks from incomplete data

Xiao Ding, Ling Wei Kong, Hai Feng Zhang, Ying Cheng Lai

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Reconstructing complex networks and predicting the dynamics are particularly challenging in real-world applications because the available information and data are incomplete. We develop a unified collaborative deep-learning framework consisting of three modules: network inference, state estimation, and dynamical learning. The complete network structure is first inferred and the states of the unobserved nodes are estimated, based on which the dynamical learning module is activated to determine the dynamical evolution rules. An alternating parameter updating strategy is deployed to improve the inference and prediction accuracy. Our framework outperforms baseline methods for synthetic and empirical networks hosting a variety of dynamical processes. A reciprocity emerges between network inference and dynamical prediction: better inference of network structure improves the accuracy of dynamical prediction, and vice versa. We demonstrate the superior performance of our framework on an influenza dataset consisting of 37 US States and a PM 2.5 dataset covering 184 cities in China.

Original languageEnglish (US)
Article number043115
JournalChaos
Volume34
Issue number4
DOIs
StatePublished - Apr 1 2024
Externally publishedYes

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • General Physics and Astronomy
  • Applied Mathematics

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