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Rejection-Based Simulation of Non-Markovian Agents on Complex Networks

  • Gerrit GroßmannEmail author
  • Luca Bortolussi
  • Verena Wolf
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)

Abstract

Stochastic models in which agents interact with their neighborhood according to a network topology are a powerful modeling framework to study the emergence of complex dynamic patterns in real-world systems. Stochastic simulations are often the preferred—sometimes the only feasible—way to investigate such systems. Previous research focused primarily on Markovian models where the random time until an interaction happens follows an exponential distribution.

In this work, we study a general framework to model systems where each agent is in one of several states. Agents can change their state at random, influenced by their complete neighborhood, while the time to the next event can follow an arbitrary probability distribution. Classically, these simulations are hindered by high computational costs of updating the rates of interconnected agents and sampling the random residence times from arbitrary distributions.

We propose a rejection-based, event-driven simulation algorithm to overcome these limitations. Our method over-approximates the instantaneous rates corresponding to inter-event times while rejection events counter-balance these over-approximations. We demonstrate the effectiveness of our approach on models of epidemic and information spreading.

Keywords

Gillespie simulation Complex networks Epidemic modeling Rejection sampling Multi-agent system 

Notes

Acknowledgements

We thank Guillaume St-Onge for helpful comments on non-Markovian dynamics. This research was been partially funded by the German Research Council (DFG) as part of the Collaborative Research Center “Methods and Tools for Understanding and Controlling Privacy”.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Gerrit Großmann
    • 1
    Email author
  • Luca Bortolussi
    • 1
    • 2
  • Verena Wolf
    • 1
  1. 1.Saarland UniversitySaarbrückenGermany
  2. 2.University of TriesteTriesteItaly

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