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Leo Y.,University of Lyon | Fleury E.,University of Lyon | Alvarez-Hamelin J.I.,University of Buenos Aires | Sarraute C.,GranData | Karsai M.,University of Lyon
Journal of the Royal Society Interface | Year: 2016

The uneven distribution of wealth and individual economic capacities are among the main forces, which shape modern societies and arguably bias the emerging social structures. However, the study of correlations between the social network and economic status of individuals is difficult due to the lack of large-scale multimodal data disclosing both the social ties and economic indicators of the same population. Here,we close this gap through the analysis of coupled datasets recording the mobile phone communications and bank transaction history of one million anonymized individuals living in a Latin American country. We show that wealth and debt are unevenly distributed among people in agreementwith the Pareto principle; the observed social structure is strongly stratified, with people being better connected to others of their own socioeconomic class rather than to others of different classes; the social network appears to have assortative socioeconomic correlations and tightly connected 'rich clubs'; and that individuals from the same class live closer to each other but commute further if they are wealthier. These results are based on a representative, society-large population, and empirically demonstrate some long-lasting hypotheses on socioeconomic correlations, which potentially lay behind social segregation, and induce differences in human mobility. © 2016 The Author(s) Published by the Royal Society. All rights reserved.


Silveira L.M.,Federal University of Minas Gerais | de Almeida J.M.,Federal University of Minas Gerais | Marques-Neto H.T.,Pontifical Catholic University of Minas Gerais | Sarraute C.,GranData | Ziviani A.,National Laboratory for Scientific Computing LNCC
Computer Communications | Year: 2016

The literature is rich in mobility models that aim at predicting human mobility. Yet, these models typically consider only a single kind of data source, such as data from mobile calls or location data obtained from GPS and web applications. Thus, the robustness and effectiveness of such data-driven models from the literature remain unknown when using heterogeneous types of data. In contrast, this paper proposes a novel family of data-driven models, called MobHet, to predict human mobility using heterogeneous data sources. Our proposal is designed to use a combination of features capturing the popularity of a region, the frequency of transitions between regions, and the contacts of a user, which can be extracted from data obtained from various sources, both separately and conjointly. We evaluate the MobHet models, comparing them among themselves and with two single-source data-driven models, namely SMOOTH and Leap Graph, while considering different scenarios with single as well as multiple data sources. Our experimental results show that our best MobHet model produces results that are better than or at least comparable to the best baseline in all considered scenarios, unlike the previous models whose performance is very dependent on the particular type of data used. Our results thus attest the robustness of our proposed solution to the use of heterogeneous data sources in predicting human mobility. © 2016.


Mucelli Rezende Oliveira E.,Ecole Polytechnique - Palaiseau | Mucelli Rezende Oliveira E.,French Institute for Research in Computer Science and Automation | Carneiro Viana A.,French Institute for Research in Computer Science and Automation | Sarraute C.,GranData | And 2 more authors.
Pervasive and Mobile Computing | Year: 2016

Understanding human mobility patterns is crucial to fields such as urban mobility and mobile network planning. For this purpose, we make use of large-scale datasets recording individuals spatio-temporal locations, from eight major world cities: Beijing, Tokyo, New York, Paris, San Francisco, London, Moscow and Mexico City. Our contributions are two-fold: first, we show significant similarities in people's mobility habits regardless of the city and nature of the dataset. Second, we unveil three persistent traits present in an individual's urban mobility: repetitiveness, preference for shortest-paths, and confinement. These characteristics uncover people's tendency to revisit few favorite venues using the shortest-path available. © 2016 Elsevier B.V.


Leo Y.,University of Lyon | Busson A.,University of Lyon | Sarraute C.,GranData | Fleury E.,University of Lyon
Ad Hoc Networks | Year: 2016

In urban areas, the population density is still growing (the population density starts exceeding 20.000 inhabitants per km2), and so, the density of mobile users becomes very important. People are moving from home to work, from work to active places. One can take benefit of the mobility and the density to justify DTN (Delay Tolerant Network) approach protocol to convey SMS (or alternative messaging services) traffic. Indeed, the mobility of users, especially during the day, create an ad hoc mobile network where the nodes are the smartphones hold by mobile clients. In this paper, their performance evaluations are based on a measurement and analysis of SMS traces coming from a nationwide cellular telecommunication operator during a two month period, we propose several DTN like basic network protocols for delivering SMS. We perform a temporal and spatial analysis of the Mexico City cellular network considering geolocalized SMS to characterize the traffic. Such key characterization allows us to answer the question: is it possible to transmit SMS using phones as relay in a large city such as Mexico City? We define four network protocols to transmit SMS from a source to a destination. We study a mobile dataset including 8 Million users living in Mexico city. This gives us a precise estimation of the average transmission time and the global performance of our approach. Our analysis shows that after 30 min, half of the SMS are delivered successfully to destination. On the contrary to the cellular networks, we explain how much the potentiality of the mobile users network can take benefit from complementary properties such as the locality of SMS, the density of phones in Mexico City and the mobility of phone users. Moreover, we show that in a realistic scenario, our approach induces reasonable storage cost. © 2016 Elsevier B.V.


Leo Y.,University of Lyon | Busson A.,University of Lyon | Sarraute C.,GranData | Fleury E.,University of Lyon
Computer Communications | Year: 2016

Cellular technologies are evolving quickly to constantly adapt to new usage and tolerate the load induced by the increasing number of phone applications. Understanding the mobile traffic is thus crucial to refine models and improve experiments. In this context, one has to understand the temporal activity of a user and the user movements. At the user scale, the usage is not only defined by the amount of calls but also by the user's mobility. At a higher level, the base stations have a key role on the quality of service. In this paper, we analyze a very large Call Detail Records (CDR) over 12 months in Mexico. It contains 8 millions users and 5 billions of call events. Our first contribution is the study call duration and inter-arrival time parameters. Then, we assess user movements between consecutive calls (switching from a station to another one). Our study suggests that user mobility is pretty dependent on user activity. Furthermore, we show properties of the inter-call mobility by making an analysis of the call distribution. © 2016 Elsevier B.V.


Leo Y.,University of Lyon | Sarraute C.,GranData | Busson A.,University of Lyon | Fleury E.,University of Lyon
PE-WASUN 2015 - Proceedings of the 12th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks | Year: 2015

In this paper, from a measurement study and analysis of sms based on traces coming from a nationwide cellular telecommunication operator during a two month period, we propose a DTN (Delay Tolerant Network) like network protocol for delivering sms. More precisely, we perform a temporal and spatial analysis of the Mexico City cellular network considering geolocalized sms. The temporal analysis allows us to detect events and to check for overloading periods, with abnormal or unexpected traffic, and to study the evolution of classical parameters such as activity or distance between source and destination. The spatial analysis is based on the Voronoï diagram of the base stations covering Mexico City. We explain how sms traffic can be characterized. Such key characterization allows us to answer the question: is it possible to transmit sms using phones as relay in a large city such as Mexico City? We defined a simple network protocol to transmit sms from a source to a destination. This DTN like protocol does not need routing nor global knowledge. The protocol takes benefit from the locality of sms, the density of phones in Mexico City and the mobility of phone users. We studied a mobile dataset including 8 millions users living in Mexico city. This gave use a precise estimation of the average transmission time and the global performance of our approach. After 30 minutes, half of the sms were delivered successfully to destination. © 2015 ACM.


PubMed | GranData, University of Buenos Aires and University of Lyon
Type: Journal Article | Journal: Journal of the Royal Society, Interface | Year: 2016

The uneven distribution of wealth and individual economic capacities are among the main forces, which shape modern societies and arguably bias the emerging social structures. However, the study of correlations between the social network and economic status of individuals is difficult due to the lack of large-scale multimodal data disclosing both the social ties and economic indicators of the same population. Here, we close this gap through the analysis of coupled datasets recording the mobile phone communications and bank transaction history of one million anonymized individuals living in a Latin American country. We show that wealth and debt are unevenly distributed among people in agreement with the Pareto principle; the observed social structure is strongly stratified, with people being better connected to others of their own socioeconomic class rather than to others of different classes; the social network appears to have assortative socioeconomic correlations and tightly connected rich clubs; and that individuals from the same class live closer to each other but commute further if they are wealthier. These results are based on a representative, society-large population, and empirically demonstrate some long-lasting hypotheses on socioeconomic correlations, which potentially lay behind social segregation, and induce differences in human mobility.


Sarraute C.,GranData | Blanc P.,University of Buenos Aires | Burroni J.,GranData
ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining | Year: 2014

Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper we focus on the population of Mexican mobile phone users. Our first contribution is an observational study of mobile phone usage according to gender and age groups. We were able to detect significant differences in phone usage among different subgroups of the population. Our second contribution is to provide a novel methodology to predict demographic features (namely age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We provide details of the methodology and show experimental results on a real world dataset that involves millions of users. © 2014 IEEE.


Ponieman N.B.,GranData | Salles A.,University of Buenos Aires | Sarraute C.,GranData
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 | Year: 2013

The massive amounts of geolocation data collected from mobile phone records has sparked an ongoing effort to understand and predict the mobility patterns of human beings. In this work, we study the extent to which social phenomena are reflected in mobile phone data, focusing in particular in the cases of urban commute and major sports events. We illustrate how these events are reflected in the data, and show how information about the events can be used to improve predictability in a simple model for a mobile phone user's location. Copyright 2013 ACM.


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