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Increasing Coverage of Information Spreading in Social Networks with Supporting Seeding

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10387))

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Abstract

Campaigns based on information spreading processes within online networks have become a key feature of marketing landscapes. Most research in the field has concentrated on propagation models and improving seeding strategies as a way to increase coverage. Proponents of such research usually assume selection of seed set and the initialization of the process without any additional support in following stages. The approach presented in this paper shows how initiation by seed set process can be supported by selection and activation of additional nodes within network. The relationship between the number of additional activations and the size of initial seed set is dependent on network structures and propagation parameters with the highest performance observed for networks with low average degree and smallest propagation probability in a chosen model.

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Acknowledgements

This work was partially supported by the National Science Centre, Poland, grant no. 2016/21/B/HS4/01562.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, ul. Żołnierska 49, 71-210, Szczecin, Poland

    Jarosław Jankowski

  2. Department of Computational Intelligence, Wrocław University of Science and Technology, Wrocław, Poland

    Radosław Michalski

Authors
  1. Jarosław Jankowski
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  2. Radosław Michalski
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Corresponding author

Correspondence to Jarosław Jankowski .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Jankowski, J., Michalski, R. (2017). Increasing Coverage of Information Spreading in Social Networks with Supporting Seeding. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_22

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  • DOI: https://doi.org/10.1007/978-3-319-61845-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61844-9

  • Online ISBN: 978-3-319-61845-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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