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  1. Home
  3. Concise Network Models Of Memory Dynamics Reveal Explainable Patterns In Path Data.
  1. Home
  3. Concise Network Models Of Memory Dynamics Reveal Explainable Patterns In Path Data.

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Concise network models of memory dynamics reveal explainable patterns in path data.

Rohit Sahasrabuddhe1,2, Renaud Lambiotte1, Martin Rosvall3

  • 1Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK.

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|October 10, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces concise networks to model complex systems with memory effects. These networks balance model size and quality, revealing large-scale patterns more simply than traditional methods.

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Area of Science:

  • Complex systems analysis
  • Network science
  • Data modeling

Background:

  • Network methods model complex systems using random walks but struggle with path data memory effects.
  • First-order Markov models are limited; higher-order models are data-intensive and prone to overfitting/underfitting.

Purpose of the Study:

  • To develop concise networks that capture memory effects in path data.
  • To balance model simplicity and accuracy for complex systems analysis.
  • To improve the modeling of real-world systems with uneven data coverage.

Main Methods:

  • Interpolating between first-order and second-order Markov models.
  • Creating state nodes to capture prominent second-order effects.
  • Proposing a transparent measure to balance model size and quality.

Main Results:

  • Concise networks effectively reveal large-scale memory patterns in synthetic and real-world systems.
  • The proposed models are significantly simpler than full second-order Markov models.
  • The approach addresses limitations of existing network methods for path data with memory.

Conclusions:

  • Concise networks offer a simplified yet effective approach to modeling complex systems with memory.
  • This method enhances interpretability and reduces data requirements compared to higher-order models.
  • The findings have implications for analyzing dynamic processes in various scientific domains.