Friendship and mobility: user movement in location-based social networks

By Eunjoon Cho, Seth A. Myers, Jure Leskovec. 2011

3,333 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics, Transportation

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

In Transportation

Citations in the corpus

Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, methodology): “We provide an instance of R in Section IV, where dependence in the Gowalla location dataset was introduced via the Gowalla social network dataset and such dependence is probabilistic instead of deterministic as in Blowfish framework [21].”
Geometric Deep Learning: Going beyond Euclidean data (2016, background): “An important application in location-based social networks is to predict the position of the user given his or her past behavior, as well as that of his or her friends [42].”
Locative media and data-driven computing experiments (2016, background): “They argue that by focusing on the check-ins around two latent states, representing work and home, urban mobility can be modelled and understood more fully by their modelling, because ‘our model can handle an arbitrary number of them [latent states]’ (Cho et al., 2011: 1086).”
PrivCheck: privacy-preserving check-in data publishing for personalized location based services (2016, background): “Previous work [15, 6, 17, 29, 34] has shown that the disclosure of such spatiotemporal user activity data may reveal sensitive information, such as a user’s identity, health status, income level, friendship or home address, etc.”
On the validity of geosocial mobility traces (2013, methodology): “Between January 2013 and July 2013, we received data from 244 users worldwide, where each user’s measurement data covered an average of 14 days.”
Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts (2016, methodology): “We evaluate different methods based on two real-world datasets belonging to two different scenarios: • Gowalla 1 (Cho, Myers, and Leskovec 2011) is a dataset from Gowalla Website, one of the biggest location-based online social networks.”
Predictability of population displacement after the 2010 Haiti earthquake (2012, background): “Recent analyzes have also shown a high correlation between the locations of people’s social contacts and their mobility patterns (29).”
Understanding user behavior in online social networks: a survey (2013, methodology): “Using cell phone location data, as well as data from two online locationbased social networks, Cho et al. [12] aim to understand the basic laws that govern human motion and dynamics.”
Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering (2014, background): “[1] develop a model of human mobility that combines periodic short range movements with travel due to the social network structure for user movement prediction.”
Finding your friends and following them to where you are (2012, methodology): “The DBN approaches are significantly better than both random and näıve baselines, and they also dominate [6]’s social mobility model (PSMM) by a large margin.”
It's the way you check-in: identifying users in location-based social networks (2014, methodology): “We find that the check-in data of the neighbors of a user, depending on the dataset being used, have a limited impact on the ability of identifying that user, which fits with what previous studies have observed on the interaction between mobility and social ties in LBSNs [6, 13, 14].”
Sequential Recommender System based on Hierarchical Attention Networks (2018, methodology): “We perform experiments on two real-world datasets, Tmall [Hu et al. , 2017] and Gowalla [Cho et al. , 2011], to demonstrate the effectiveness of our model.”
Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (2017, methodology): “e Gowalla check-in dataset [7] was generated world-wide from February 2009 to October 2010.”
Location-based and preference-aware recommendation using sparse geo-social networking data (2012, background): “It is similar to the discovery in [5] that a long-distance travel is more inﬂuenced by the social network ties.”
Recommendations in location-based social networks: a survey (2015, methodology): “[19] propose a location recommendation method based on the periodicity of the human movement.”
Geo-located Twitter as proxy for global mobility patterns (2013, methodology): “Keywords: geo-located Twitter; global mobility patterns; community detection; collective sensing”
Instilling Social to Physical: Co-Regularized Heterogeneous Transfer Learning (2016, background): “The studies in the field of LBSN either tackle link prediction or user profiling on social side with the help of locations (Cho et al. 2011; Scellato and Mascolo 2011), or address location or activity recommendation on physical side with the help of social media (Bhargava et al. 2015; Gao et al.…”
Hierarchical block structures and high-resolution model selection in large networks (2013, background): “5 Political Blogs [67] 16 Brightkite social network [78] 27 APS citationsd 6 arXiv Co-Authors (gr-qc) [70] 17 Epinions.”
A Survey on Deep Learning for Human Mobility (2020, methodology): “VANext is evaluated using ACC@k on the datasets of Gowalla [34] and Foursquare for Singapore and New York City.”