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Unraveling hidden interactions in complex systems with deep learning.

Seungwoong Ha1, Hawoong Jeong2,3

  • 1Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Korea.

Scientific Reports
|June 18, 2021
PubMed
Summary
This summary is machine-generated.

AgentNet, a novel deep learning framework, uncovers hidden interactions in complex systems using only observed data. This model-free approach accurately identifies agent behaviors and interaction strengths, advancing complex systems analysis.

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Area of Science:

  • Complex Systems Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Modeling micro-dynamics and interactions in complex systems is challenging for traditional data-driven methods.
  • Human ingenuity is often required to establish interaction models.
  • Existing approaches struggle to infer hidden interaction patterns solely from observed data.

Purpose of the Study:

  • To introduce AgentNet, a model-free deep learning framework for revealing and analyzing hidden interactions in complex systems.
  • To demonstrate AgentNet's capability to infer interactions from observed data alone.
  • To provide a novel approach for process-driven modeling in complex systems.

Main Methods:

  • AgentNet employs a graph attention network with variable-wise attention for modeling agent interactions.
  • The framework utilizes flexible encoders and decoders applicable to diverse systems.
  • Deep neural networks form the core of this data-driven framework.

Main Results:

  • AgentNet successfully modeled discrete (cellular automata), continuous (Vicsek model), and non-Markovian (active Ornstein-Uhlenbeck particles) complex systems.
  • Visualized attention values in AgentNet correlated with true interaction strengths and revealed emergent collective behaviors.
  • Empirical data from bird flocks demonstrated AgentNet's ability to detect hidden interaction ranges beyond conventional analysis.

Conclusions:

  • AgentNet offers a powerful data-driven framework for investigating complex systems without prior model assumptions.
  • The model accurately identifies interactions and emergent behaviors, even those absent in training data.
  • This approach opens new avenues for understanding and modeling complex phenomena across various scientific domains.