Abstract

 

The explosion of mobile phone communications in the last years occurs at a moment where data processing power increases exponentially. Thanks to those two changes in a global scale, the road has been opened to use mobile phone communications to generate inferences and characterizations of mobile phone users. In this work, we use the communication

network, enriched by a set of users’ attributes, to gain a better understanding of the demographic features of a population. Namely, we use call detail records and banking information to infer the income of each person in the graph.

Abstract

 

A map of potential prevalence of Chagas disease (ChD) with high

spatial disaggregation is presented. It aims to detect areas outside

the Gran Chaco ecoregion (hyperendemic for the ChD), characterized by high affinity with ChD and high health vulnerability. To quantify potential prevalence, we developed several indicators: an Affinity Index which quantifies the degree of linkage between endemic areas of ChD and the rest of the country. We also studied favorable habitability conditions for Triatoma infestans, looking for areas where the predominant materials of floors, roofs and internal ceilings favor the presence of the disease vector. We studied determinants of a more general nature that can be encompassed under the concept of Health Vulnerability Index. These determinants are associated with access to health providers and the socio-economic level of different segments of the population. Finally we constructed a Chagas Potential Prevalence Index (Ch-PPI) which combines the affinity index, the health vulnerability index, and the population density. We show and discuss the maps obtained. These maps are intended to assist public health specialists, decision makers of public health policies and public officials in the development of cost-effective strategies to improve access to diagnosis and treatment of ChD.

Abstract

 

Our aim is for Wibson to be a blockchain-based, decentralized data marketplace that provides individuals a way to securely and anonymously sell information in a trusted environment. The combination of the Wibson token and blockchain-enabled smart contracts hopes to allow Data Sellers and Data Buyers to transact with each other directly while providing individuals the ability to maintain anonymity

as desired. Wibson intends that its data marketplace will provide infrastructure and financial incentives for individuals to securely sell personal information without sacrificing personal privacy. Data Buyers receive information from willing and actively participating individuals with the benefit of knowing that the personal information should be accurate

and current.

Abstract

 

Can we predict the future success of a product, service, or business by monitoring the behavior of a small se of individuals? A positive answer would have important implications for the science of success and managerial practices, yet recent works have supported diametrically opposite answers. To resolve this tension, we address this question in a unique, large-scale dataset that combines individuals' purchasing history with their social and mobility traits across an entire nation. Surprisingly, we find that the purchasing history alone enables the detection of small sets of \discoverers" whose early purchases consistently predict success. In contrast with the assumptions by most existing studies on word-of-mouth processes, the social hubs selected by network

centrality are not consistently predictive of success. Our approach to detect key individuals has promise for applications in other research areas including science of science, technological forecasting, and behavioral finance.

Abstract

 

In this paper we investigate the behavioural differences between mobile phone customers with prepaid and postpaid subscriptions. Our study reveals that (a) postpaid customers are more active in terms of service usage and (b) there are strong structural correlations in the mobile phone call network as connections between customers of the same subscription type are much more frequent than those between customers of different subscription types. Based on these observations we provide methods to detect the subscription type of customers by using information about their personal call statistics, and also their egocentric networks simultaneously. The key of our first approach is to cast this classification problem as a problem of graph labelling, which can be solved by max-flow min-cut algorithms. Our experiments show that, by using both user attributes and relationships, the proposed graph labelling approach is able to achieve a classification accuracy of _ 87%, which outperforms by _ 7% supervised learning methods using only user attributes. In our second problem we aim to infer the subscription type of customers of external operators. We propose via approximate methods to solve this problem by using node attributes, and a two-ways indirect inference method based on observed homophilic structural correlations. Our results have straightforward applications in behavioural prediction and personal marketing