Location-Based Social Network Data Generation Based on Patterns of Life
Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN data sets 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-scal...
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Published in | 2020 21st IEEE International Conference on Mobile Data Management (MDM) pp. 158 - 167 |
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Main Authors | , , , , , , , |
Format | Conference Proceeding |
Language | English |
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IEEE
01.06.2020
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Abstract | Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN data sets 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 synthetic towns and 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. |
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AbstractList | Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN data sets 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 synthetic towns and 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. |
Author | Jin, Hyunjee Crooks, Andrew Kavak, Hamdi Wenk, Carola Zufle, Andreas Rouly, Ovi Chris Kim, Joon-Seok Pfoser, Dieter |
Author_xml | – sequence: 1 givenname: Joon-Seok surname: Kim fullname: Kim, Joon-Seok organization: George Mason University,Geography and Geoinformation Science – sequence: 2 givenname: Hyunjee surname: Jin fullname: Jin, Hyunjee organization: George Mason University,Geography and Geoinformation Science – sequence: 3 givenname: Hamdi surname: Kavak fullname: Kavak, Hamdi organization: George Mason University,Computational and Data Science – sequence: 4 givenname: Ovi Chris surname: Rouly fullname: Rouly, Ovi Chris organization: Tulane University,Computer Science – sequence: 5 givenname: Andrew surname: Crooks fullname: Crooks, Andrew organization: George Mason University,Computational and Data Science – sequence: 6 givenname: Dieter surname: Pfoser fullname: Pfoser, Dieter organization: George Mason University,Geography and Geoinformation Science – sequence: 7 givenname: Carola surname: Wenk fullname: Wenk, Carola organization: Tulane University,Computer Science – sequence: 8 givenname: Andreas surname: Zufle fullname: Zufle, Andreas organization: George Mason University,Geography and Geoinformation Science |
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Snippet | Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN data sets yields several weaknesses:... |
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SubjectTerms | Data Generation Location-Based Social Networks Patterns of Life Social Network Data Generation Social Simulation Temporal Social Network Data Trajectory Data Generation |
Title | Location-Based Social Network Data Generation Based on Patterns of Life |
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