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Agent Based ModelsgisLBSNMASONSocial Networks

Location-Based Social Network Data Generation

Publicly Available Real-World LBSN Data Sets.
To overcome these weaknesses in this paper we present a LBSN simulation (an agent-based model created in MASON) capable of creating multiple
artificial but socially plausible, large-scale LBSN data sets. If this sounds of interest to you, below we provide a little more information about the paper, Specifically, its abstract, a depiction of LBSNs, our case studies and the resulting simulations we used to develop LBSN data based on patterns of life (PoL) and some sample results. At the bottom of this post we provde the full reference and link to the paper. 


Location-based social networks (LBSNs) have been
studied extensively in recent years. However, utilizing real-world
LBSN data sets in such studies yields several weaknesses: sparse
and small data sets, privacy concerns, and a lack of authoritative
ground-truth. To overcome these weaknesses, we leverage a large scale
LBSN simulation to create a framework to simulate human
behavior and to create synthetic but realistic LBSN data based on
human patterns of life. Such data not only captures the location
of users over time but also their interactions via social networks.
Patterns of life are simulated by giving agents (i.e., people) an
array of “needs” that they aim to satisfy, e.g., agents go home
when they are tired, to restaurants when they are hungry, to
work to cover their financial needs, and to recreational sites
to meet friends and satisfy their social needs. While existing
real-world LBSN data sets are trivially small, the proposed
framework provides a source for massive LBSN benchmark
data that closely mimics the real-world. As such it allows us
to capture 100% of the (simulated) population without any
data uncertainty, privacy-related concerns, or incompleteness.
It allows researchers to see the (simulated) world through the
lens of an omniscient entity having perfect data. Our framework
is made available to the community. In addition, we provide a
series of simulated benchmark LBSN data sets using different
real-world urban environments obtained from OpenStreetMap.
The simulation software and data sets which comprise gigabytes
of spatio-temporal and temporal social network data are made
available to the research community.

LBSN Overview

Case Studies: A: New Orleans, Louisiana (NOLA), Mississippi River, Lake Pontchartrain, and the ‘French Quarter’. B: George Mason University (GMU), Fairfax, VA. C: Synthetic Villages – Small (Left) and Large (Right).
Environments Populated with Agents. Clockwise from Top Left: GMU, NOLA, Large and Small Synthetic Villages.
Data Sets Resulting from Location-Based Social Network Simulation
Average Social Network Degree over Time (1K).
Social Network

Full Reference:

Kim, J-S., Jin, H., Kavak, H., Rouly, O.C., Crooks, A.T., Pfoser, D., Wenk, C. and Züfle, A. (2020), Location-Based Social Network Data Generation Based on Patterns of Life, The 21st IEEE International Conference on Mobile Data Management, Versailles, France. (pdf)

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