Skip to main content
SpringerLink
Log in

Models of Influence Spreading on Social Networks

  • Conference paper
  • First Online:
Complex Networks & Their Applications X (COMPLEX NETWORKS 2021)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1073))

Included in the following conference series:

  • 3660 Accesses

Abstract

Complex contagion (CC) and simple contagion (SC) models have been used to describe influence and information spreading on social networks. We investigate four different models, two from each category, and discuss the interplay between the characteristics of social interactions and different variants of the models. Different properties of social networks are discovered by calculating centrality measures with the proposed spreading models. We demonstrate the results with the Zachary’s Karate Club social network and a larger Facebook social media network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Barrat, A., Barthélemy, M., Vespignani, A.: Dynamical processes on complex networks. University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  2. Borgatti, S.P.: Centrality and network flow. Social Networks 27(1), 55–71 (2005)

    Article  MathSciNet  Google Scholar 

  3. Centola, D.: How behavior spreads, the science of complex contagions. Princeton University Press (2018)

    Google Scholar 

  4. Centola, D., May, M.: Complex contagions and the weakness of long ties. American Journal of Sociology 113 (2007)

    Google Scholar 

  5. Dey K., Lamba H., Nagar S., Gupta S., Kaushik S.: Modeling Topical Information Diffusion over Microblog Networks. In: Aiello L., Cherifi C., Cherifi H., Lambiotte R., Lió P., Rocha L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. (2019). https://doi.org/10.1007/978-3-030-05411-3_29

  6. Ijäs, M., Levijoki, J., Kuikka, V.: Scalable algorithm for computing influence spreading probabilities in social networks. 5th European conference on social media, Limerick institute of technology (ECMS) (2018)

    Google Scholar 

  7. Joshi, A., Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A.A.: Social Ties and their Relevance to Churn in Mobile Telecom Networks. 11th International Conference on Extending Database Technology (EDBT’08) (2008)

    Google Scholar 

  8. Kempe, D., Kleinberg, J., Tardéos, E.: Maximizing the spread of influence453 through a social network. Theory of Computing 11, 105–147 (2015)

    Article  MathSciNet  Google Scholar 

  9. Kirkley, A., Cantwell, G.T., Newman, M.E.J.: Belief propagation for networks with loops. Science Advances 7 (2021)

    Google Scholar 

  10. Kuikka, V.: Influence spreading model used to analyse social networks. Computational Social Networks 5, 12 (2018). https://doi.org/10.1186/s40649-018-0060-z

  11. Kuikka, V.: Modelling community structure and temporal spreading on complex networks. Computational Social Networks 8(1), 1–38 (2021). https://doi.org/10.1186/s40649-021-00094-z

    Article  Google Scholar 

  12. Kuikka, V.: Modelling Epidemic Spreading in Structured Organisations. Submitted to Physica A (2021)

    Google Scholar 

  13. Leskovec, J., Krevl, A.: SNAP Datasets, Stanford Large Network Dataset Collection (2014). http://snap.stanford.edu/data

  14. Li, K., Zhang, H., Huang, H.: Social influence analysis: models, methods, and evaluation. Engineering 4 (2018)

    Google Scholar 

  15. Min, B., Miguel, M.S.: Competing contagion processes: Complex contagion triggered by simple contagion. Sci Rep. 8, 10422 (2018)

    Article  Google Scholar 

  16. Newman, M.E.J.: A measure of betweenness centrality based on random walks. Social Networks 27(1) 39-54 (2005). https://doi.org/10.1016/j.socnet.2004.11.009

  17. White, S., Smyth, P.: Algorithms for estimating relative importance in networks. Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ’03) 266-275 (2003). https://doi.org/10.1145/956750.956782

  18. Zachary W.W.: An information flow model for conflict and fission in small groups 33 452-473. Journal of Anthropological Research (1973)

    Google Scholar 

Download references

Acknowledgements

We are thankful to B.Sc. Juhana Valasmo for his idea of the Diff-sc algorithm in describing diffusion processes on social networks.

Author information

Authors and Affiliations

  1. Finnish Defence Research Agency, Tykkikentäntie 1, PO BOX 10, 11311, Riihimäki, Finland

    Vesa Kuikka & Minh An Antti Pham

Authors
  1. Vesa Kuikka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minh An Antti Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vesa Kuikka .

Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas Universidad Politécnica de Madrid, Universidad Politécnica de Madrid, Madrid, Madrid, Spain

    Rosa Maria Benito

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

    Chantal Cherifi

  3. LE2I, UFR Sciences et Techniques, University of Burgundy, Dijon, France

    Hocine Cherifi

  4. Grupo Interdisciplinar de Sistemas Compl, Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Center for Complex Networks and Systems Research School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

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

    Marta Sales-Pardo

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kuikka, V., Pham, M.A.A. (2022). Models of Influence Spreading on Social Networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-93413-2_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93412-5

  • Online ISBN: 978-3-030-93413-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation