Socio-Spatial Properties of Online Location-Based Social Networks

By Salvatore Scellato, Anastasios Noulas, Renaud Lambiotte, Cecilia Mascolo. International Conference on Web and Social Media, 2011

499 citations recorded by the source corpus.

The spatial structure of large-scale online social networks has been largely unaccessible due to the lack of available and accurate data about people’s location. However, with the recent surging popularity of location-based social services, data about the geographic position of users have been available for the first time, together with their online social connections. In this work we present a comprehensive study of the spatial properties of the social networks arising among users of three main popular online location-based services. We observe robust universal features across them: while all networks exhibit about 40% of links below 100 km, we further discover strong heterogeneity across users, with different characteristic spatial lengths of interaction across both their social ties and social triads. We provide evidence that mechanisms akin to gravity models may influence how these social connections are created over space. Our results constitute the first large-scale study to unravel the socio-spatial properties of online location-based social networks.

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

View Paper: https://www.semanticscholar.org/paper/7dd530663e0b084a5aad9a55cec021d8331514d3

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Citations in the corpus

Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks (2012, background): “Contrarily, LBSNs data are in large-scale, but very sparse (Noulas et al. 2011; Scellato et al. 2011).”
Recommendations in location-based social networks: a survey (2015, methodology): “[86] analyzes the data collected from a location-based social networking system (Foursquare) and finds that 1) about 40% of the connections are within 100 km, 2) a strong heterogeneity exists across users regarding the spatial distances of connections between their social ties and triads, and 3) gra”
Content-Aware Point of Interest Recommendation on Location-Based Social Networks (2015, methodology): “Due to the strong correlations between geographical distance and social connections discovered in previous work (Cheng et al. 2011; Cho, Myers, and Leskovec 2011; Scellato et al. 2011; Noulas et al. 2011; Zhang et al. 2012; Gao, Tang, and Liu 2012; Long and Joshi 2013; Chang and Sun 2011), current w”
Locative media and data-driven computing experiments (2016, methodology): “Some tests suggested that the social model might explain the relationships between social ties and space better when, for example, results showed that social relations can appear regardless of the distance between the individuals involved (Scellato et al., 2011: 334).”
The social distributional hypothesis: a pragmatic proxy for homophily in online social networks (2014, unknown): “More related work on correlation of the probability of social ties and the geographic distance of the corresponding users can be found in Scellato et al. (2011), McGee et al. (2011), and Kaltenbrunner et al. (2012).”
Measuring user activity on an online location-based social network (2011, background): “have studied their spatial properties, describing how a considerable fraction of users exhibit a prevalence of short-distance spatially-clustered social ties and how such user heterogeneity appears driven by a mixture of social and geographic factors [14], [15].”
Exploring temporal effects for location recommendation on location-based social networks (2013, background): “The large-scale check-in data on LBSNs is usually very sparse due to the user-driven check-in property [19, 17, 8].”
Finding your friends and following them to where you are (2012, background): “The relationship between social ties and distance has recently received considerable attention [20, 2, 25].”
Human mobility: Models and applications (2017, background): “[68] analyzed three online location-based social networks: Foursquare, Brightkite, and Gowalla, finding their exhibition of common properties found in most other real world complex networks, such as fat tailed degree distributions, high clustering coefficient, and short average path length between n”
Location-based and preference-aware recommendation using sparse geo-social networking data (2012, background): “This phenomenon is quite common in the real world [20], aggravating the data sparse problem to location rating inference (if we want to provide people from NYC with location recommendations in LA).”
Smart cities of the future (2012, unknown): “Content analysis allows semantically-rich analysis of land use [41], temporal variability of current-moment interests within cities and related travel behaviours [37], and inter-city comparisons which reveal common dependencies in human mobility across countries and continents [34].”
LCARS: a location-content-aware recommender system (2013, background): “This observation of travel locality is quite common in the real world [22], aggravating the data sparsity problem with personalized spatial item recommendations (e.”
A Tale of Many Cities: Universal Patterns in Human Urban Mobility (2011, background): “Moreover, these data appear to exhibit properties similar to those in mobile phone cellular data [8,9].”
Location based services: ongoing evolution and research agenda (2018, background): “…enables people to better understand and model human mobility (Shaw, Tsou, and Xinyue 2016; Yuan, Raubal, and Liu 2012), geosocial networks (Scellato et al. 2011), city dynamics, and semantics (Gao, Janowicz, and Couclelis 2017), as well as to monitor and optimise tra ﬃ c (Keler 2017),…”
The Length of Bridge Ties: Structural and Geographic Properties of Online Social Interactions (2012, background): “Overall, the evidence is that social factors or spatial factors alone cannot capture the properties of real-world social systems over space (Scellato et al. 2011).”