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Layer Communities in Multiplex Networks

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Abstract

Multiplex networks are a type of multilayer network in which entities are connected to each other via multiple types of connections. We propose a method, based on computing pairwise similarities between layers and then doing community detection, for grouping structurally similar layers in multiplex networks. We illustrate our approach using both synthetic and empirical networks, and we are able to find meaningful groups of layers in both cases. For example, we find that airlines that are based in similar geographic locations tend to be grouped together in a multiplex airline network and that related research areas in physics tend to be grouped together in a multiplex collaboration network.

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Notes

  1. In his original paper, Sampson used the terms Affect, Esteem, Sanctioning, and Influence (and their counterparts). We use the labels “Praise(−)” and “Like(\(+\))” instead of “Sanctioning” and “Affect”, respectively, in this article.

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Acknowledgements

We thank Peter Mucha and participants in University of Oxford’s Networks Journal Club for helpful comments.

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Authors and Affiliations

  1. Department of Physics, University of Oxford, Oxford, UK

    Ta-Chu Kao

  2. Department of Mathematics, University of California Los Angeles, Los Angeles, USA

    Mason A. Porter

  3. Mathematical Institute, University of Oxford, Oxford, UK

    Mason A. Porter

  4. CABDyN Complexity Centre, University of Oxford, Oxford, UK

    Mason A. Porter

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  1. Ta-Chu Kao
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Correspondence to Mason A. Porter.

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Kao, TC., Porter, M.A. Layer Communities in Multiplex Networks. J Stat Phys 173, 1286–1302 (2018). https://doi.org/10.1007/s10955-017-1858-z

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  • DOI: https://doi.org/10.1007/s10955-017-1858-z

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