Harnessing Mobile Phone Social Network Topology to Infer Users Demographic Attributes

Brea, Jorge; Burroni, Javier; Minnoni, Martín; Sarraute, Carlos

doi:10.1145/2659480.2659492

Cited by 15 publications

(10 citation statements)

References 14 publications

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“…The accuracy is in the 70-80% range in all cases. A similar approach is adopted by Brea et al [35], who focus on age prediction of 74 million Mexican citizens. By using the correlation between demographic properties of users that are connected in the mobile call graph, the authors successfully classify up to 72% of the population into four age categories.…”

Section: Demographicsmentioning

confidence: 99%

“…Krings et al [51] group mobile customers by their billing address, and obtain a communication network between 571 cities in Belgium. By studying this graph, the authors show that inter-city communication follows a gravity model 35 . This result thus corroborates that mobile communication distance tends to be heavy tailed.…”

Section: Rr N°mentioning

confidence: 99%

“…where (x,ỹ) are the coordinates of the community geographical center, and (x i , y i ) are the geographical coordinates of a user i belonging to the community. 35 The gravity model defines the communication intensity c ij between two cities i and j as c…”

Section: Rr N°mentioning

confidence: 99%

See 2 more Smart Citations

Large-Scale Mobile Traffic Analysis: A Survey

Naboulsi

Fiore

Ribot

et al. 2016

IEEE Commun. Surv. Tutorials

215

161

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This article surveys the literature on analyses of mobile traffic collected by operators within their network infrastructure. This is a recently emerged research field, and, apart from a few outliers, relevant works cover the period from 2005 to date, with a sensible densification over the last three years. We provide a thorough review of the multidisciplinary activities that rely on mobile traffic datasets, identifying major categories and sub-categories in the literature, so as to outline a hierarchical classification of research lines. When detailing the works pertaining to each class, we balance a comprehensive view of state-ofthe-art results with punctual focuses on the methodological aspects. Our approach provides a complete introductory guide to the research based on mobile traffic analysis. It allows summarizing the main findings of the current state-of-the-art, as well as pinpointing important open research directions.

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Section: Demographicsmentioning

confidence: 99%

Section: Rr N°mentioning

confidence: 99%

See 1 more Smart Citation

Large-Scale Mobile Traffic Analysis: A Survey

Naboulsi

Fiore

Ribot

et al. 2016

IEEE Commun. Surv. Tutorials

215

161

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“…For each user u ∈ A we are given the age u a of the user, which allows us to observe differences in the income distribution according to the age. In another line of work, homophily with respect to age has been observed and used to generate inferences [2]. Fig.…”

Section: B Banking Informationmentioning

confidence: 99%

A Bayesian approach to income inference in a communication network

Fixman¹,

Berenstein²,

Brea³

et al. 2016

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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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. arXiv:1811.04246v1 [cs.CY]

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“…The social graph constructed from the communications between mobile phone users is rich in information that was leveraged to make inferences such as dynamic communities [23], inferences of age, gender and other socio-demographic attributes [24,25,26,27,5]. In particular it provides an effective tool for predicting customer churn [28,29,30], along with a variety of other predictions [31,32,33,34,35].…”

Section: Introduction 1a Bit Of Historymentioning

confidence: 99%

Snel: SQL Native Execution for LLVM

Mottalli¹,

Sanchez²,

Ajzenman³

et al. 2020

Preprint

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Snel is a relational database engine featuring Just-In-Time (JIT) compilation of queries and columnar data representation. Snel is designed for fast on-line analytics by leveraging the LLVM compiler infrastructure. It also has custom special methods like resolving histograms as extensions to the SQL language. "Snel" means "SQL Native Execution for LLVM".Unlike traditional database engines, it does not provide a client-server interface. Instead, it exposes its interface as an extension to SQLite, for a simple interactive usage from command line and for embedding in applications. Since Snel tables are read-only, it does not provide features like transactions or updates. This allows queries to be very fast since they don't have the overhead of table locking or ensuring consistency.At its core, Snel is simply a dynamic library that can be used by client applications. It has an SQLite extension for seamless integration with a traditional SQL environment and simple interactive usage from command line.

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Harnessing Mobile Phone Social Network Topology to Infer Users Demographic Attributes

Cited by 15 publications

References 14 publications

Large-Scale Mobile Traffic Analysis: A Survey

Large-Scale Mobile Traffic Analysis: A Survey

A Bayesian approach to income inference in a communication network

Snel: SQL Native Execution for LLVM

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