Bridging the gap between physical location and online social networks

By Justin Cranshaw, Eran Toch, Jason I. Hong, Aniket Kittur, Norman M. Sadeh. Ubiquitous Computing, 2010

503 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e635e55c0e3ebd87940d7d9c681c9d242c486a4e

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

In Transportation

Citations in the corpus

Friendship and mobility: user movement in location-based social networks (2011, background): “While traditionally records of calls made by cell phones have been used to track the location of the cell phone towers associated with the calls [12, 17, 30], location-based social networks [15, 29, 28] provide an important new dimension in understanding human mobility.”
Measuring Large-Scale Social Networks with High Resolution (2014, background): “Finally, in the Locaccino study [36], location within a metropolitan region was recorded for 489 participants for varying periods, ranging from seven days to several months.”
Location-based and preference-aware recommendation using sparse geo-social networking data (2012, background): “The other is the entropy of each level, which can effectively capture the diversity of a user’s preferences [7], as shown in Equation 7, where H ( u, l ) is user u ’s entropy at level l and P ( c ) is the probability that u visited category c in her historical data.”
An Empirical Study of Geographic User Activity Patterns in Foursquare (2011, background): “The authors in (Cranshaw et al. 2010) improve link prediction based on co-occurrences by taking into account contextual information such as the entropy of a location, a metric that accounts for the diversity of unique visitors to an area.”
GeoCrowd: enabling query answering with spatial crowdsourcing (2012, unknown): “Location entropy was ﬁrst introduced in [16].”
Exploiting place features in link prediction on location-based social networks (2011, methodology): “Our prediction features are based on user check-ins and on the concept of “place entropy” [5], which is used to discriminate venues that are more or less likely to foster social connections (Section 4).”
Urban computing with taxicabs (2011, background): “, estimating the similarity between users in terms of their location histories [2][3][9], extracting social structures from mobile phone data [4], enabling friend and location recommenders in the real world [17][18], and studying the influence of pervasive systems on people in urban spaces [8].”
DeepMove: Predicting Human Mobility with Attentional Recurrent Networks (2018, methodology): “In order to evaluate the variation of performance gain among different users, we cluster them into different groups based on three rules: mobility entropy [8], explore ratio, radius of gyration [10].”
The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City (2012, unknown): “As data from location sharing systems are becoming increasingly available for researchers to analyze, there have been a number of recent results from social science, computer science, and machine learning that are finding new ways to extract various insights on relations between online and offline i”
Recommendations in location-based social networks: a survey (2015, background): “Location histories provide rich contextual information and have significant correlations to real social behaviors [22].”
Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things (2013, background): “Researchers from CMU study the relationship between the users’ mobility patterns and structural properties of the online social network, to identify the implicit social link between physical interaction and online connection [25].”