NationTelescope: Monitoring and visualizing large-scale collective behavior in LBSNs

By Dingqi Yang, Daqing Zhang, Longbiao Chen, Bingqing Qu. 2015

161 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics, Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistical and Nonlinear Physics

1. Philippe Cudré-Mauroux
2. Vitaly Shmatikov
3. Congzheng Song
4. Dingqi Yang
5. Bingqing Qu
6. Sebastian Garcia
7. Maria Rigaki
8. Daqing Zhang

In Transportation

1. Philippe Cudré-Mauroux
2. Vitaly Shmatikov
3. Congzheng Song
4. Dingqi Yang
5. Bingqing Qu
6. Sebastian Garcia
7. Maria Rigaki
8. Daqing Zhang

Citations in the corpus

• Exploiting Unintended Feature Leakage in Collaborative Learning (2018, neutral): “In [63, 64], Yang et al.”
• Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks (2016, background): “[Yang et al. 2015] studied the spatio-temporal patterns of user activities in LBSNs.”
• Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (2017, background): “1% [24, 45], while the density of Netƒix data for movie recommendation is around 1.”
• Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach (2019, methodology): “• Temporal activeness [36]: We model a user’s check-in data by a vector encoding the user’s check-in frequency over 168 hours in a week (as shown in Figure 2(c)), and then measure the Cosine distance between two users’ temporal activeness vectors.”
• Inference Attacks Against Collaborative Learning (2018, methodology): “/www.yelp.com/dataset 6 that maps it to a sequence of hidden vectors. We add a fully connected classification layer to the last hidden vector of the sequence. SGD learning rate is 0.05. FourSquare. In [63,64], Yang et al. collected a global dataset of FourSquare location “check-ins” (userID, time, lo”
• A Survey of Privacy Attacks in Machine Learning (2020, methodology): “Foursquare [103] mixed features binary classification [61, 79, 85] 528,878”
• PrivCheck: privacy-preserving check-in data publishing for personalized location based services (2016, methodology): “We use a check-in dataset collected from Foursquare by [40, 41].”
• ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models (2018, unknown): “Concretely, we use a shadow model trained on the Location dataset to attack a target model trained on the Purchase-100 dataset.”