Abstract

Location-based social discovery networks (LBSD) is an emerging category of location-based social networks (LBSN) that are specifically designed to enable users to discover and communicate with nearby people. In this paper, we present the first measurement study of the characteristics and evolution of location-based communities which are based on a social discovery network and geographic proximity. We measure and analyse more than 176K location-based communities with over 1.4 million distinct members of a popular social discovery network and more than 46 million locations. We characterise the evolution of the communities and study the user behaviour in LBSD by analysing the mobility features of users belonging to communities in comparison to non-community members. Using observed spatio-temporal similarity features, we build and evaluate a classifier to predict location-based community membership solely based on user mobility information.

Cite

@article{thilakarathna2017deep,
  title={A deep dive into location-based communities in social discovery networks},
  author={Thilakarathna, Kanchana and Seneviratne, Suranga and Gupta, Kamal and Kaafar, Mohamed Ali and Seneviratne, Aruna},
  journal={Computer Communications},
  volume={100},
  pages={78--90},
  year={2017},
  publisher={Elsevier},
  url={https://kampta.github.io/lbsd},
}