Reality mining: sensing complex social systems

By Nathan Eagle, Alex Pentland. Personal and Ubiquitous Computing, 2006

3,052 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Transportation

View Paper: https://www.semanticscholar.org/paper/99d2b2b6368d995744205c13c6b11e69024a5205

How researchers see this paper

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

In Computer Science Applications

In Computer Vision and Pattern Recognition

In Transportation

Citations in the corpus

Measuring Large-Scale Social Networks with High Resolution (2014, methodology): “For computational social science using Bluetooth-based detection of participants’ devices as a proxy for face-to-face interactions is a well-established method [19,29,31].”
A survey of mobile phone sensing (2010, neutral): “Although the potential of using mobile phones as a platform for sensing research has been discussed for a number of years now, in both industrial [8] and research communities [9, 10], there has been little or no advancement in the field until recently.”
Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones (2010, neutral): “For the short term and everyday mobility, more ﬂexible methods such as various registers and indirect databases (Raymer et al. , 2007), satellite-based methods (Chen et al. , 2006), or modern sensing technologies (Kwan, 2000; Eagle and Pentland, 2005; Shoval, 2007) are needed.”
Mining interesting locations and travel sequences from GPS trajectories (2009, background): “Eagle et al [ 7 ] aimed to recognize the social pattern in daily user activity from the dataset collected by 100 users with a Bluetooth-enabled mobile phone.”
Routing in Socially Selfish Delay Tolerant Networks (2010, methodology): “The evaluation is based on the MIT Reality trace [22], [23], on which a recent work [24] has validated the existence of the so-called “small-world” phenomenon, a well-known phenomenon in social networks.”
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, neutral): “The MIT dataset provides the mobility of 94 students in the MIT Campus for 246 days [249].”
NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems (2011, background): “In addition, temporal rhythms of human behavior have been studied and modeled to discover daily activity patterns, to infer relationships and to determine significant locations [7].”
An Analysis of Visitors' Behavior in the Louvre Museum: A Study Using Bluetooth Data (2014, methodology): “Examples include measuring the relationship between peoples’ social networks (Eagle and Pentland, 2005; Paulos and Goodman, 2004), analyzing mobility of pedestrians (Delafontaine et al, 2012; Kostakos et al, 2010; Versichele et al, 2012), and estimating travel times (Barceló et al, 2010).”
Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data (2010, background): “With the extensive records of mobile phone data such as calling pattern and location of the mobile phone user, analyses have been performed on numerous aspects including behavioral routine [7][8][9], social proximity [10][11], call prediction [12][13], social closeness [14][15], and human mobility [”
Recruitment Framework for Participatory Sensing Data Collections (2010, methodology): “A day is chosen as the representative time period while a week is the time span based on previous work on human location patterns [24,49].”
Using ghost edges for classification in sparsely labeled networks (2008, positive): “We present results on four real-world data sets: political book co-purchases [9], Enron emails [4], Reality Mining cell-phone calls [5], and high-energy physics citations from arXiv (a.”
Distributed community detection in delay tolerant networks (2007, background): “Pocket Switched Network (PSN) [1] is a kind of Delay Toler-ant Networks (DTN) [6] that provides intermittent communication for humans carrying mobile radio devices.”
Time-varying graphs and dynamic networks (2010, methodology): “The formalism introduced therein to represent dynamic graphs has been used as a framework for several works such as [6,27,47,65].”
Towards rich mobile phone datasets: Lausanne data collection campaign (2010, background): “The increased resolution was thought to be particularly linked to yielding richer location based data, in comparison to the Reality Mining project, which operated on a more narrow range of attributes.”
Recommendations in location-based social networks: a survey (2015, background): “, [29, 31], have suggest that a user’s historical behaviors is a powerful indicator of the user’s preferences.”