Inferring social ties from geographic coincidences

By David J. Crandall, Lars Backstrom, Dan Cosley, Siddharth Suri, Dan Huttenlocher, Jon M. Kleinberg. Proceedings of the National Academy of Sciences of the United States of America, 2010

499 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/69850ad4116d5ba0e41dfe53bccfa581f6371312

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In Statistical and Nonlinear Physics

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Citations in the corpus

DeepCity: A Feature Learning Framework for Mining Location Check-Ins (2016, methodology): “ user check-ins. With the large amount of user check-in data being available, researchers have concentrated on mining these data. One direction is to use user checkins to predict friendships, such as [4, 21]. Meanwhile, a few works have explored a user’s friends information to predict his locations ”
An Empirical Study of Geographic User Activity Patterns in Foursquare (2011, background): “In particular, inferring friendships in a social network through the exploitation of location information such as user geographic co-occurrences has been suggested (Crandall et al. 2010; Eagle, Pentland, and Lazer 2009).”
Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data (2013, background): “and geographical impacts on social networks [4,5] are investigated.”
Structural diversity in social contagion (2012, methodology): “We can use these tags to deduce whether two unlinked nodes in a contact neighborhood have been jointly tagged in any photos, a property we refer to as “co-tagging,” which serves as an indication of a social tie through copresence at an event (21).”
Latent Space Model for Multi-Modal Social Data (2015, background): “Previous studies modeled social networks and user mobility patterns [14, 17, 50, 53].”
An Analysis of Anonymity in the Bitcoin System (2011, background): “[16] infer social ties between users where none are explicitly stated by looking at patterns of ‘co-incidences’ or common off-network cooccurences.”
Finding your friends and following them to where you are (2012, background): “explore the relationship between co-location of Flickr users and their social ties [7].”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, background): “A first group of works tries to understand the interplay of mobility patterns and social ties [Cho et al. 2011; Crandall et al. 2010; Pan et al. 2013; Wang et al. 2011].”
Uncovering space-independent communities in spatial networks (2010, background): “A dominant factor for focus constraint is geographic proximity, which oﬀers opportunities for face-to-face interaction and encounters between individuals [52, 53].”
State of the Geotags: Motivations and Recent Changes (2017, background): “…has sprung from this wealth of publicly available geotagged posts, such as understanding demographics and social dynamics in cities (Mohammady and Culotta 2014), ﬁnding home locations of individuals (Jurgens, Mccorriston, and Ruths 2015), and inferring likely friends (Crandall et al. 2010).”
How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations (2012, background): “cial media platforms to study a multitude of social, economic, and political phenomena. For example, social media data have recently been employed to infer social ties based on geographical proximity [1], study how diurnal and seasonal mood patterns match up with the eects of sleep and circadian rhy”
A Tale of Many Cities: Universal Patterns in Human Urban Mobility (2011, background): “improve engineered systems such as cloud computing and location-based recommendations [2, 3, 4, 5], enhance research in social networks [6, 7, 8, 9] and yield insight into a variety of important societal issues, such as urban planning and epidemiology [10, 11, 12].”
Exploiting place features in link prediction on location-based social networks (2011, background): “[4] shows how temporal and spatial co-occurences between people help to infer social ties among them: while their main goal is to put forward a generative model which explains empirical data, our study has a different aim, that is, designing a link prediction system to be used on realworld location-”
Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr (2013, background): “The similarity of spatial and temporal information associated with photos provided by different contributors may even indicate the probability of a social tie between them (Crandall et al. 2010).”
Urban tribes: Analyzing group photos from a social perspective (2012, background): “The analysis of this imagery offers the potential for fruitful interaction between computer science and psychological sociology [4] [8].”
Friendship and mobility: user movement in location-based social networks (2011, background): “Studies have also explored how social networks are embed into the underlying geography [22 , 19, 29], and how geo-data can be used to infer social ties [5, 8, 6] .”