Article | Published:

Hiding individuals and communities in a social network

Nature Human Behaviourvolume 2pages139147 (2018) | Download Citation

Abstract

The Internet and social media have fuelled enormous interest in social network analysis. New tools continue to be developed and used to analyse our personal connections, with particular emphasis on detecting communities or identifying key individuals in a social network. This raises privacy concerns that are likely to exacerbate in the future. With this in mind, we ask the question ‘Can individuals or groups actively manage their connections to evade social network analysis tools?’ By addressing this question, the general public may better protect their privacy, oppressed activist groups may better conceal their existence and security agencies may better understand how terrorists escape detection. We first study how an individual can evade ‘node centrality’ analysis while minimizing the negative impact that this may have on his or her influence. We prove that an optimal solution to this problem is difficult to compute. Despite this hardness, we demonstrate how even a simple heuristic, whereby attention is restricted to the individual’s immediate neighbourhood, can be surprisingly effective in practice; for example, it could easily disguise Mohamed Atta’s leading position within the World Trade Center terrorist network. We also study how a community can increase the likelihood of being overlooked by community-detection algorithms. We propose a measure of concealment—expressing how well a community is hidden—and use it to demonstrate the effectiveness of a simple heuristic, whereby members of the community either ‘unfriend’ certain other members or ‘befriend’ some non-members in a coordinated effort to camouflage their community.

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Acknowledgements

M.W. was supported by the Polish National Science Centre (grant 2015/17/N/ST6/03686). M.J.W. was supported by the European Research Council under Advanced Grant 291528 (‘RACE’). T.P.M. was supported by the Polish National Science Centre (grant 2014/13/B/ST6/01807) and, for the earlier versions of this article, also by the European Research Council under Advanced Grant 291528 (‘RACE’). No funders had any role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

  1. Institute of Informatics, University of Warsaw, Warsaw, Poland

    • Marcin Waniek
    •  & Tomasz P. Michalak
  2. Department of Computer Science, Khalifa University of Science and Technology, Abu Dhabi, UAE

    • Marcin Waniek
    •  & Talal Rahwan
  3. Department of Computer Science, University of Oxford, Oxford, UK

    • Tomasz P. Michalak
    •  & Michael J. Wooldridge

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Contributions

T.P.M., M.J.W. and T.R. conceived the study and designed the experiments. M.W. and T.P.M. formalized the computational problems. M.W. and T.R. developed the heuristics. M.W. developed the proofs and performed the numerical simulations. All authors discussed the results and wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Tomasz P. Michalak or Talal Rahwan.

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    Supplementary Notes, Supplementary Figures 1–92, Supplementary References 1–33

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DOI

https://doi.org/10.1038/s41562-017-0290-3