Finding your friends and following them to where you are

By Adam Sadilek, Henry A. Kautz, Jeffrey P. Bigham. Web Search and Data Mining, 2012

373 citations recorded by the source corpus.

Subfields: Epidemiology, Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/9e813279b6e2b6b4d6bf6e979ba2b3ae5dcc4cfd

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In Epidemiology

In Statistical and Nonlinear Physics

In Transportation

Citations in the corpus

Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics" (2012, background): “Data drawn from mobile devices on the whereabouts of their users has led to an influx of interesting findings in explaining patterns of human mobility [6, 17, 5], predicting friendship on social networking sites based on location data [23, 20], and better understanding different aspects of cities, b”
Supervised Text-based Geolocation Using Language Models on an Adaptive Grid (2012, background): “Sadilek et al. (2012) discretize by simply clustering data points within a small distance threshold, but only perform geolocation within ﬁxed city limits.”
Diffusion of Lexical Change in Social Media (2012, background): “hical communities as an important factor driving the observable diusion of lexical change. Individuals who are geographically proximate will indeed be more likely to share social network connections [61], so the role of geography in our analysis is not dicult to explain. But more surprising is the r”
Mining User Mobility Features for Next Place Prediction in Location-Based Services (2012, background): “A different approach that exploits social networking information to infer the current location of a user has been proposed in [9].”
Where You Like to Go Next: Successive Point-of-Interest Recommendation (2013, neutral): “Although some work consider the temporal information, they mainly focus on POI prediction or next place prediction on the existing locations or users’ historical trajectories [Gao et al. , 2012a; Sadilek et al. , 2012; Sadilek and Krumm, 2012; Sang et al. , 2012; Gao et al. , 2012b].”
Where in the World Are You? Geolocation and Language Identification in Twitter* (2013, methodology): “For instance, Sadilek, Kautz, and Bigham (2012) demonstrated that when the location of a subset of users is known, it is then possible to infer the location of the friends of these users.”
Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach (2019, methodology): “[25] manually combined features from social proximity, mobility similarity, and textual similarity extracted from users’ Tweets for friendship prediction.”
Latent Space Model for Multi-Modal Social Data (2015, background): “Previous studies modeled social networks and user mobility patterns [14, 17, 50, 53].”