  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.
