The European Physical Journal B

, 86:290

Social and place-focused communities in location-based online social networks


    • Computer Laboratory, University of Cambridge
  • Vincenzo Nicosia
    • Computer Laboratory, University of Cambridge
    • School of Mathematical Sciences, Queen Mary University of London
  • Salvatore Scellato
    • Computer Laboratory, University of Cambridge
  • Anastasios Noulas
    • Computer Laboratory, University of Cambridge
  • Cecilia Mascolo
    • Computer Laboratory, University of Cambridge
Regular Article

DOI: 10.1140/epjb/e2013-40253-6

Cite this article as:
Brown, C., Nicosia, V., Scellato, S. et al. Eur. Phys. J. B (2013) 86: 290. doi:10.1140/epjb/e2013-40253-6


Thanks to widely available, cheap Internet access and the ubiquity of smartphones, millions of people around the world now use online location-based social networking services. Understanding the structural properties of these systems and their dependence upon users’ habits and mobility has many potential applications, including resource recommendation and link prediction. Here, we construct and characterise social and place-focused graphs by using longitudinal information about declared social relationships and about users’ visits to physical places collected from a popular online location-based social service. We show that although the social and place-focused graphs are constructed from the same data set, they have quite different structural properties. We find that the social and location-focused graphs have different global and meso-scale structure, and in particular that social and place-focused communities have negligible overlap. Consequently, group inference based on community detection performed on the social graph alone fails to isolate place-focused groups, even though these do exist in the network. By studying the evolution of tie structure within communities, we show that the time period over which location data are aggregated has a substantial impact on the stability of place-focused communities, and that information about place-based groups may be more useful for user-centric applications than that obtained from the analysis of social communities alone.


Statistical and Nonlinear Physics
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© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2013