Letter | Published:

Social influence maximization under empirical influence models

Nature Human Behaviourvolume 2pages375382 (2018) | Download Citation

Abstract

Social influence maximization models aim to identify the smallest number of influential individuals (seed nodes) that can maximize the diffusion of information or behaviours through a social network. However, while empirical experimental evidence has shown that network assortativity and the joint distribution of influence and susceptibility are important mechanisms shaping social influence, most current influence maximization models do not incorporate these features. Here, we specify a class of empirically motivated influence models and study their implications for influence maximization in six synthetic and six real social networks of varying sizes and structures. We find that ignoring assortativity and the joint distribution of influence and susceptibility leads traditional models to underestimate influence propagation by 21.7% on average, for a fixed seed set size. The traditional models and the empirical types that we specify here also identify substantially different seed sets, with only 19.8% overlap between them. The optimal seeds chosen under empirical influence models are relatively less well-connected and less central nodes, and they have more cohesive, embedded ties with their contacts. Hence, empirically motivated influence models have the potential to identify more realistic sets of key influencers in a social network and inform intervention designs that disseminate information or change attitudes and behaviours.

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Change history

  • 02 August 2018

    In the version of this Letter originally published, in the key for Fig. 1 the red square was mistakenly labelled ‘Low influence’ and ‘High susceptibility’ but should have been labelled ‘High influence’ and ‘Low susceptibility’. This has now been corrected.

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Acknowledgements

We thank to D. Eckles for invaluable discussions. S.A. acknowledges funding and support from the NSF (Career Award 0953832). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

  1. MIT Sloan School of Management, Cambridge, MA, USA

    • Sinan Aral
    •  & Paramveer S. Dhillon

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Contributions

S.A. and P.S.D. contributed equally to all parts of the research and writing.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Sinan Aral or Paramveer S. Dhillon.

Supplementary information

  1. Supplementary Information

    Supplementary Methods, Supplementary Note 1, Supplementary Figures 1–58

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DOI

https://doi.org/10.1038/s41562-018-0346-z