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  1. Home
  3. Predicting Variable-length Paths In Networked Systems Using Multi-order Generative Models.
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  3. Predicting Variable-length Paths In Networked Systems Using Multi-order Generative Models.

Related Experiment Video

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Predicting variable-length paths in networked systems using multi-order generative models.

Christoph Gote1,2,3, Giona Casiraghi1, Frank Schweitzer1

  • 1Chair of Systems Design, ETH Zurich, Zurich, Switzerland.

Applied Network Science
|September 25, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces MOGen, a novel generative modeling framework for accurately predicting sequences in networked systems. MOGen outperforms existing methods for path prediction and modeling complex system dynamics.

Keywords:
Graph miningSequence predictionSequential pattern miningSupervised learning

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Data Science

Background:

  • Networked systems generate path data, which are temporally ordered sequences of nodes constrained by topology.
  • Understanding path patterns is crucial for analyzing complex systems and optimizing engineered systems like supply chains and mobility services.

Purpose of the Study:

  • To introduce MOGen, a generative modeling framework for accurate next-element and out-of-sample prediction of paths.
  • To develop a parameter-free model selection approach that automatically identifies the optimal model from data.
  • To establish a mathematical formalism linking path models to random walks in multi-layer networks.

Main Methods:

  • Developed MOGen, a generative modeling framework for path prediction.
  • Implemented an automated model selection approach for parameter-free optimization.
  • Introduced a mathematical framework connecting higher-order path models to multi-layer network random walks.

    • Main Results:

    • MOGen demonstrates high accuracy and consistency in both next-element and out-of-sample path prediction.
    • Empirical data shows MOGen surpasses current state-of-the-art sequence modeling techniques.
    • A novel mathematical formalism was established linking path models to random walks in multi-layer networks.

    Conclusions:

    • MOGen provides an effective and automated solution for modeling and predicting paths in complex networked systems.
    • The framework advances the understanding of structure and dynamics in complex systems through accurate path analysis.
    • The developed mathematical formalism offers new insights into the relationship between path data and network dynamics.