Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach

Yang, Dingqi; Qu, Bingqing; Yang, Jie; Cudré-Mauroux, Philippe

doi:10.1145/3308558.3313635

Cited by 211 publications

(152 citation statements)

References 40 publications

(57 reference statements)

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“…We used the United States region from Global-scale Check-in Dataset [26]. It has over 12 million check-ins by about 400 thousand users at about 2 million locations over a period of 22 months (from Apr.…”

Section: Data Acquisition/preparationmentioning

confidence: 99%

Ensemble mobility predictor based on random forest and Markovian property using LBSN data

Araújo

Machado

et al. 2020

J Internet Serv Appl

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The ubiquitous connectivity of Location-Based Systems (LBS) allows people to share individual location-related data anytime. In this sense, Location-Based Social Networks (LBSN) provides valuable information to be available in large-scale and low-cost fashion via traditional data collection methods. Moreover, this data contains spatial, temporal, and social features of user activity, enabling a system to predict user mobility. In this sense, mobility prediction plays crucial roles in urban planning, traffic forecasting, advertising, and recommendations, and has thus attracted lots of attention in the past decade. In this article, we introduce the Ensemble Random Forest-Markov (ERFM) mobility prediction model, a two-layer ensemble learner approach, in which the base learners are also ensemble learning models. In the inner layer, ERFM considers the Markovian property (memoryless) to build trajectories of different lengths, and the Random Forest algorithm to predict the user’s next location for each trajectory set. In the outer layer, the outputs from the first layer are aggregated based on the classification performance of each weak learner. The experimental results on the real user trajectory dataset highlight a higher accuracy and f1-score of ERFM compared to five state-of-the-art predictors.

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Section: Data Acquisition/preparationmentioning

confidence: 99%

Ensemble mobility predictor based on random forest and Markovian property using LBSN data

Araújo

Machado

et al. 2020

J Internet Serv Appl

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“… Foursquare check-ins. We use check-in data 25 , 26 from the Foursquare mobile application, which captures snapshots of human mobility in popular public spaces. The dataset includes seven major cities across the world (New York, Chicago, Los Angeles, London, Tokyo, Istanbul, and Jakarta), and contains a total of 2,293,716 check-ins from 24,068 individuals in 397,610 venues over a period of 140 days.…”

Section: Methodsmentioning

confidence: 99%

Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data

Wang

Ghosh

Ding

et al. 2021

Sci Rep

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Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.

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“…In addition, there are many studies related to measuring travel destination estimation, such as real-time destination estimation [32][33], online trajectory compression [34], and personalized destination estimation [35]. In addition, Yang [36][37] et al researches location prediction problems based on Location Based Social Networks (LBSNs), and has achieved excellent experimental results.…”

Section: A Location Predictionmentioning

confidence: 99%

Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones

Han

et al. 2020

IEEE Access

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Tracking moving objects in a city, such as suspicious vehicles or persons, is important for public safety management. Traditionally, target tracking is assisted by the pre-deployed stationary surveillance cameras, which are with insufficient coverage. In this work, we propose a different approach called Co-Tracking, a real-time target tracking system that leverages both citizens' mobile phones and stationary surveillance cameras to track moving objects collaboratively. Two key techniques are focused. Firstly, in order to accurately assign tracking tasks, we propose the Middle Query Location Prediction (MQLP) algorithm for predicting the target's location. Secondly, in order to efficiently utilizes these human/machine resources, we propose a heuristic algorithm, namely S-Maximum, to optimize the task allocation, including maximizing the number of completed tracking tasks and minimizing the number of mobile phones. Experimental results show that the proposed Co-Tracking system can effectively track moving objects with low incentive costs.

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Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach

Cited by 211 publications

References 40 publications

Ensemble mobility predictor based on random forest and Markovian property using LBSN data

Ensemble mobility predictor based on random forest and Markovian property using LBSN data

Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data

Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones

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