Friday, July 26, 2019

Location-Based Social Simulation

At the upcoming 16th International Symposium on Spatial and Temporal Databases (SSTD) we have vision paper entitled "Location-Based Social Simulation" accepted. In the paper we discuss issues such as data sparsity and privacy concerns with using real world location-based social networks (LBSNs) like Foursquare and Yelp. To overcomes these issues, we describe how one can employ geospatial simulation (i.e. an agent-based model) to create artificial, but socially plausible LBSN data sets which overcomes some of the limitations with respect to LBSNs.

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 geosimulation 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 very large 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 allowing us to develop solutions to improve LBSN applications such as user-location recommendation, friend recommendation, location prediction, and location privacy.

KEYWORDS: Agent-based simulation, location-based social network, data generator, spatial network, human behavior

Full Reference: 
Kavak, H., Kim, J-S., Crooks, A.T., Pfoser, D., Wenk C. and Z├╝fle, A (2019), Location-Based Social Simulation, Proceedings of the 16th International Symposium on Spatial and Temporal Databases, Vienna, Austria, pp 218-221. (pdf)

Update: Our paper was selected as runner-up  for best Vision Paper.

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