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Extent prediction of the information and influence propagation in online social networks

Author

Listed:
  • Raúl M. Ortiz-Gaona

    (Polytechnic University of Catalonia (UPC)
    Universidad de Cuenca)

  • Marcos Postigo-Boix

    (Polytechnic University of Catalonia (UPC))

  • José L. Melús-Moreno

    (Polytechnic University of Catalonia (UPC))

Abstract

We present a new mathematical model that predicts the number of users informed and influenced by messages that are propagated in an online social network. Our model is based on a new way of quantifying the tie-strength, which in turn considers the affinity and relevance between nodes. We could verify that the messages to inform and influence, as well as their importance, produce different propagation behaviors in an online social network. We carried out laboratory tests with our model and with the baseline models Linear Threshold and Independent Cascade, which are currently used in many scientific works. The results were evaluated by comparing them with empirical data. The tests show conclusively that the predictions of our model are notably more accurate and precise than the predictions of the baseline models. Our model can contribute to the development of models that maximize the propagation of messages; to predict the spread of viruses in computer networks, mobile telephony and online social networks.

Suggested Citation

  • Raúl M. Ortiz-Gaona & Marcos Postigo-Boix & José L. Melús-Moreno, 2021. "Extent prediction of the information and influence propagation in online social networks," Computational and Mathematical Organization Theory, Springer, vol. 27(2), pages 195-230, June.
    • Handle: RePEc:spr:comaot:v:27:y:2021:i:2:d:10.1007_s10588-020-09309-6
    DOI: 10.1007/s10588-020-09309-6
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    References listed on IDEAS

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