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Related Experiment Video

Updated: Jul 10, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Bayes-optimal inference for spreading processes on random networks.

Davide Ghio1, Antoine L M Aragon2, Indaco Biazzo2

  • 1Ide PHICS Laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Rte Cantonale, 1015 Lausanne, Switzerland.

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|November 18, 2023
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Summary
This summary is machine-generated.

This study introduces a message-passing algorithm for inferring network spreading dynamics, generalizing epidemic models. The algorithm achieves Bayes-optimal performance under specific conditions, offering insights into network science and computational inference.

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Area of Science:

  • Statistical physics
  • Network science
  • Computational inference

Background:

  • Spreading processes on networks are fundamental to understanding phenomena like epidemics.
  • Existing models (e.g., SIR, SIS) have limitations in capturing complex dynamics.
  • Inferring these dynamics from partial observations is a significant challenge.

Purpose of the Study:

  • To develop and analyze a message-passing inference algorithm for network spreading processes.
  • To determine if the algorithm achieves Bayes-optimal performance using Nishimori conditions.
  • To investigate phase transitions and algorithm convergence on random networks.

Main Methods:

  • Generalization of epidemic models (SIR, SIS) for spreading processes.
  • Message-passing inference algorithm derived from belief propagation (BP) equations.
  • Analysis using Nishimori conditions to assess Bayes-optimality.
  • Probing phase transitions via convergence time and initialization strategies.

Main Results:

  • The BP algorithm demonstrates Bayes-optimal performance in large parameter regions, closely satisfying Nishimori conditions.
  • Algorithm convergence and optimality are observed even for moderate system sizes.
  • In other parameter regions, the BP algorithm struggles to converge, attributed to finite-size effects rather than phase transitions.

Conclusions:

  • The developed message-passing algorithm offers an effective and often optimal approach for inferring network spreading dynamics.
  • Nishimori conditions serve as a reliable indicator of Bayes-optimality in these inference problems.
  • Understanding parameter-dependent performance and finite-size effects is crucial for applying the algorithm effectively.