A survey of results on mobile phone datasets analysis

Blondel, Vincent D.; Decuyper, Adeline; Krings, Gautier

doi:10.48550/arxiv.1502.03406

Cited by 6 publications

(7 citation statements)

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“…Because of the above, mobile phone call records have been used in numerous studies on diverse topics [2]: social network structure (e.g. [3,4]), geography of social relationships (e.g.…”

Section: Introductionmentioning

confidence: 99%

From seconds to months: an overview of multi-scale dynamics of mobile telephone calls

2015

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Big Data on electronic records of social interactions allow approaching human behaviour and sociality from a quantitative point of view with unforeseen statistical power. Mobile telephone Call Detail Records (CDRs), automatically collected by telecom operators for billing purposes, have proven especially fruitful for understanding one-to-one communication patterns as well as the dynamics of social networks that are reflected in such patterns. We present an overview of empirical results on the multi-scale dynamics of social dynamics and networks inferred from mobile telephone calls. We begin with the shortest timescales and fastest dynamics, such as burstiness of call sequences between individuals, and "zoom out" towards longer temporal and larger structural scales, from temporal motifs formed by correlated calls between multiple individuals to long-term dynamics of social groups. We conclude this overview with a future outlook.

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“…Because of the above, mobile phone call records have been used in numerous studies on diverse topics [2]: social network structure (e.g. [3,4]), geography of social relationships (e.g.…”

Section: Introductionmentioning

confidence: 99%

From seconds to months: an overview of multi-scale dynamics of mobile telephone calls

2015

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“…In this way, mobile phones provide approximate spatio-temporal localization of users and create an immense resource for the analysis of human mobility and behavioral patterns. [6][7][8] In a burst of new applications built on mobile phone data, 9 we emphasize those of great practical importance such as urban planning, 10 disaster management, 11 transportation mode inference, 12 traffic engineering, 13 deriving poverty indicators 14 and crime prediction. 15 Currently, there is a growing interest in the mining of mobile phone data for epidemiological purposes.…”

Section: Introductionmentioning

confidence: 99%

Unveiling Spatial Epidemiology of HIV with Mobile Phone Data

Brdar

Gavric

Ćulibrk

et al. 2016

Sci Rep

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An increasing amount of geo-referenced mobile phone data enables the identification of behavioral patterns, habits and movements of people. With this data, we can extract the knowledge potentially useful for many applications including the one tackled in this study - understanding spatial variation of epidemics. We explored the datasets collected by a cell phone service provider and linked them to spatial HIV prevalence rates estimated from publicly available surveys. For that purpose, 224 features were extracted from mobility and connectivity traces and related to the level of HIV epidemic in 50 Ivory Coast departments. By means of regression models, we evaluated predictive ability of extracted features. Several models predicted HIV prevalence that are highly correlated (>0.7) with actual values. Through contribution analysis we identified key elements that correlate with the rate of infections and could serve as a proxy for epidemic monitoring. Our findings indicate that night connectivity and activity, spatial area covered by users and overall migrations are strongly linked to HIV. By visualizing the communication and mobility flows, we strived to explain the spatial structure of epidemics. We discovered that strong ties and hubs in communication and mobility align with HIV hot spots.

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“…This active development of the field is largely attributed to the huge amount of digital information that has become available due to rapid development of the information and communication technology (ICT). Many empirical studies have been conducted on social networks including those based on email [3], mobile phone call (MPC) [4][5][6], short-message communication, social networking services (SNS) [7,8], and scientific collaborations [9]. Among these, MPC data sets play a special role because mobile phones are frequently used in our daily life and the coverage of the service is almost 100% for adults in large part of the world.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Role of Relationship Fading and Breakup in Social Network Formation

Murase

Török

et al. 2015

PLoS ONE

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In social networks of human individuals, social relationships do not necessarily last forever as they can either fade gradually with time, resulting in “link aging,” or terminate abruptly, causing “link deletion,” as even old friendships may cease. In this paper, we study a social network formation model where we introduce several ways by which a link termination takes place. If we adopt the link aging, we get a more modular structure with more homogeneously distributed link weights within communities than when link deletion is used. By investigating distributions and relations of various network characteristics, we find that the empirical findings are better reproduced with the link deletion model. This indicates that link deletion plays a more prominent role in organizing social networks than link aging.

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A survey of results on mobile phone datasets analysis

Cited by 6 publications

References 0 publications

From seconds to months: an overview of multi-scale dynamics of mobile telephone calls

From seconds to months: an overview of multi-scale dynamics of mobile telephone calls

Unveiling Spatial Epidemiology of HIV with Mobile Phone Data

Modeling the Role of Relationship Fading and Breakup in Social Network Formation

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