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Crying “Wolf” in a Network Structure: The Influence of Node-Generated Signals

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Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 881))

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Abstract

Research into rumor spreading in a social network has largely assumed that information may only originate from an external content provider. In today’s age individual nodes may also be content providers. The propagation of a signal generated by a node in the network may contribute or diminish the efforts of information diffusion, as signals become an imprecise indication of a node’s knowledge.

We present a model that allows for incorporating node-generated information into the well-studied area of modeling rumor spread in a network. We capture this by a stochastic information transmission mechanism at each node, with a positive probability to spread the rumor without holding its value. Simulations are performed using synthetic Watts-Strogatz networks, along with a real-world Facebook sample graph. Using decision trees as a descriptive tool, we examine the effects of the rate in which internal non-informed nodes generate information on the properties of the rumor spread process.

As our main results we show that: increasing the rate of information generated by non-informed nodes may have monotonous or non-monotonous influence on the rumor spread time, in dependency with whether the network is sparse on not. We also identify that a strategy of increasing external communication in order to gain higher pureness level tends to be effective only for a medium level range of this generation rate and only in sparse networks.

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Acknowledgements

We thank Dr. Ilan Gronau from the Interdisciplinary Center Computer Science School for his knowledgeable and useful remarks.

Author information

Authors and Affiliations

  1. Efi Arazi School of Computer Science, IDC Herzliya, P.O. Box 167, 4610101, Herzliya, Israel

    Tomer Tuchner

  2. Adelson School of Entrepreneurship, IDC Herzliya, P.O. Box 167, 4610101, Herzliya, Israel

    Gail Gilboa-Freedman

Authors
  1. Tomer Tuchner
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  2. Gail Gilboa-Freedman
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Corresponding author

Correspondence to Gail Gilboa-Freedman .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

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Tuchner, T., Gilboa-Freedman, G. (2020). Crying “Wolf” in a Network Structure: The Influence of Node-Generated Signals. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_25

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