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Stimulation Index of Cascading Transmission in Information Diffusion over Social Networks

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Book cover Complex Networks & Their Applications IX (COMPLEX NETWORKS 2020 2020)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 943))

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Abstract

Analyzing and modeling of information diffusion on social networks is essential because social networking sites (SNSs) have become crucial information infrastructures. In particular, “Influence Maximization,” the extraction of information source nodes that deliver information to as many users as possible on a network, has been widely researched. However, actual information diffusion is caused not only propagation according to the network structure, but also a local rise in “trending” topics. We therefore focused on the edges that cause a chain of information transmission, regardless of the number of people who received the information. Based on the information cascade, where information is propagated in chains between nodes on a network, we propose the Stimulation Index to quantify how much edges affect the subsequent transmission of information. We also evaluate the proposed index using an artificial network and verify that it is effective.

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Authors and Affiliations

  1. University of Tsukuba, Tsukuba, Ibaraki, 305-8550, Japan

    Kazufumi Inafuku & Tetsuji Satoh

  2. Tokyo University of Technology, Hachioji, Tokyo, 192-0982, Japan

    Takayasu Fushimi

Authors
  1. Kazufumi Inafuku
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  2. Takayasu Fushimi
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  3. Tetsuji Satoh
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Corresponding author

Correspondence to Kazufumi Inafuku .

Editor information

Editors and Affiliations

  1. Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, Madrid, Madrid, Spain

    Rosa M. Benito

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LIB, UFR Sciences et Techniques, Université de Bourgogne, Dijon, France

    Hocine Cherifi

  4. Grupo Interdisciplinar de Sistemas Complejos, Departamento de Matematicas, Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

  6. Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Tarragona, Spain

    Marta Sales-Pardo

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Inafuku, K., Fushimi, T., Satoh, T. (2021). Stimulation Index of Cascading Transmission in Information Diffusion over Social Networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_39

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  • DOI: https://doi.org/10.1007/978-3-030-65347-7_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65346-0

  • Online ISBN: 978-3-030-65347-7

  • eBook Packages: EngineeringEngineering (R0)

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