Location-based social simulation

Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN datasets in such studies has severe weaknesses: sparse and small datasets, privacy concerns, and a lack of authoritative ground-truth. Our vision is to create a large scale geo-simulation framework to simulate human behavior and to create synthetic but realistic LBSN data that captures the location of users over time as well as social interactions of users in a social network. While existing LBSN datasets are trivially small, such a framework would provide the first source of massive LBSN benchmark data which would closely mimic the real world, containing high-fidelity information of location, and social connections of millions of simulated agents over several years of simulated time. Therefore, it would serve the research community by revitalizing and reshaping research on LBSNs by allowing researchers to see the (simulated) world through the lens of an omniscient entity having perfect data. These evaluations will guide future research enabling us to develop solutions to improve LBSN applications such as user-location recommendation, friend recommendation, location prediction, and location privacy.