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Propagation Phenomena in Real World Networks pp 329–359Cite as

Maximizing Social Influence in Real-World Networks—The State of the Art and Current Challenges

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 85))

Abstract

The following chapter aims to present the current research in the area of modelling and maximizing social influence in networks. Apart from describing the most popular models for this process, it focuses on presenting the advances in maximizing the spread of influence in social networks . Since most of the research was suited for static networks case, nowadays it is necessary to move it toward the networks that are everywhere around us—the dynamic ones. As is widely agreed in the scientific community, static networks are unacceptable simplification of the real world processes, so current research is moving toward the temporal networks. It is especially important when modelling propagation phenomena, such as the spread of influence, epidemics or diffusion of innovations. In this chapter it is presented how the research on maximizing the spread of influence is starting to explore real-world cases and how the early attempts of solving this problem for temporal networks look like. Moreover, it is shown how to benefit from the temporal properties of the social network in order to achieve better results for spread of influence compared to the static approach.

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Notes

  1. 1.

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Acknowledgments

This work was partially supported by the fellowship co-financed by the European Union within the European Social Fund, by the European Commission under the 7th Framework Programme, Coordination and Support Action, Grant Agreement Number 316097, ENGINE—European research centre of Network intelliGence for INnovation Enhancement (http://engine.pwr.wroc.pl) and by The National Science Centre, the decision no. DEC-2013/09/B/ST6/02317. The calculations were carried out in Wroclaw Centre for Networking and Supercomputing (http://www.wcss.wroc.pl), grant No 177.

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  1. Department of Computational Intelligence, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Radosław Michalski & Przemysław Kazienko

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  1. Radosław Michalski
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  2. Przemysław Kazienko
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Correspondence to Radosław Michalski .

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Editors and Affiliations

  1. Department of Information Systems, Wrocław University of Technology, Wrocław, Poland

    Dariusz Król

  2. School of Design, Engineering and Computing, Bournemouth University, Fern Barrow Poole, United Kingdom

    Damien Fay

  3. Computational Intelligence Research Group, Bournemouth University Smart Technology Research Centre, Poole, United Kingdom

    Bogdan Gabryś

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Michalski, R., Kazienko, P. (2015). Maximizing Social Influence in Real-World Networks—The State of the Art and Current Challenges. In: Król, D., Fay, D., Gabryś, B. (eds) Propagation Phenomena in Real World Networks. Intelligent Systems Reference Library, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-319-15916-4_14

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