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Model-Checking Information Diffusion in Social Networks with PRISM

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Multi-Agent Systems and Agreement Technologies (EUMAS 2020, AT 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12520))

Abstract

In this paper we present an agent-based approach to formalising information diffusion using Markov models which attempts to account for the internal informational state of the agent and investigate the use of probabilistic model-checking for analysing these models. We model information diffusion as both continuous and discrete time Markov chains, using the latter to provide an agent-centred perspective. We present a negative result - we conclude that current model-checking technology is inadequate for analysing such systems in an interesting way.

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Notes

  1. 1.

    This is detailed in http://www.prismmodelchecker.org/doc/semantics.pdf.

  2. 2.

    Ernst Moritz Hahn, private communication.

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Acknowledgements

The research was partly supported by the project “Better Video workflows via Real-Time Collaboration and AI-Techniques in TV and New Media”, funded by the Research Council of Norway under Grant No.:269790.

Author information

Authors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Louise A. Dennis

  2. University of Bergen, Bergen, Norway

    Marija Slavkovik

Authors
  1. Louise A. Dennis
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  2. Marija Slavkovik
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Corresponding author

Correspondence to Marija Slavkovik .

Editor information

Editors and Affiliations

  1. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Nick Bassiliades

  2. Technical University of Crete, Chania, Greece

    Georgios Chalkiadakis

  3. IIIA-CSIC, Bellaterra, Spain

    Dave de Jonge

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Dennis, L.A., Slavkovik, M. (2020). Model-Checking Information Diffusion in Social Networks with PRISM. In: Bassiliades, N., Chalkiadakis, G., de Jonge, D. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2020 2020. Lecture Notes in Computer Science(), vol 12520. Springer, Cham. https://doi.org/10.1007/978-3-030-66412-1_30

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  • DOI: https://doi.org/10.1007/978-3-030-66412-1_30

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