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  • BOOK OFABSTRACTSPOSTER

    5-7 APRIL 2017 VODAFONE VILLAGE

    MILAN

    Editors:

    Francesco CalabreseEsteban MoroVincent BlondelAlex Sandy Pentland

  • INDEX5-7 A

    PRIL 2017 / VO

    DA

    FON

    E VILLA

    GE / M

    ILAN

    POSTER

    POSTER SESSION 1 5 APRIL 2017

    1. Churn Prediction in the Telecommunication Industry using Social Network Analytics p.4 Maria Oskarsdottir

    2. Joint Spatial and Temporal Classification of Mobile Traffic Demands p.7 Marco Fiore

    3. Understanding mall visiting patterns and mobility in Santiago de Chile with CDR data p.10 Mariano Beir

    4. Developing and Deploying a Taxi Price Comparison Mobile App in the Wild: Insights and Challenges p.13 Vsevolod Salnikov

    5. The Cost of Taxing Network Goods: Evidence from Mobile Phones in Rwanda p.16 Daniel Bjorkegren

    6. PRIVAMOV: Analysing Human Mobility Through Multi-Sensor Datasets p.19 Antoine Boutet

    7. Developing a mobility monitoring application hand in hand with the end user p.21 Jerome Urbain

    8. Effects of Network Architecture on Model Performance when Predicting Churn in Telco p.25 Maria Oskarsdottir

    9. A Neural Network Framework for Next Place Prediction p.27 Langford Chad

    10. Explorative analyse of two Italian cities: Turin and Venice for diversity p.30 Didem Gundogdu

    11. PyMobility: an open source Python package for human mobility analysis and simulation p.33 Gianni Barlacchi

    12. An exploratory analysis of ethnic groups in the city of Milan through mobile phone data p.35 Gianni Barlacchi

    13. Determininig an optimal time window for roaming data for tourism statistics p.38 Martijn Tennekesi

    14. Measuring transnational population mobility with roaming data p.42 Rein Ahas

    POSTER SESSION 2 6 APRIL 2017

    1. Climate change induced migrations from a cell phone perspective p.46 Sibren Isaacman

    2. High speed analysis of volatile mobile data applied to road safety p.48 Jos Gmez Castao

    3. Context-Aware Recognition of Physical Activities Using Mobile Devices p.51 Gabriele Civitarese

    4. Mining the Air -- for Research in Social Science and Networking Measurement p.53 Gabriele Civitarese

    5. User Authentication with Neural Networks Based on CDR Data p.57 Bartosz Perkowski

    6. Analysis of Tourist Activity from Cellular Network Data p.60 Marco Mamei

    7. A neural embedding approach to recommender systems in telecommunication p.63 Nikolaos Lamprou

    8. Drivers of spatial heterogeneity of HIV prevalence in Senegal: disentangling key features of human activity and mobility p.66 Lorenzo Righetto

    9. Automatic stress assessment using smartphone interaction data p.67 Matteo Ciman

    10. A New Point Process Model for the Spatial Distribution of Cell Towers p.70 Carlos Sarraute

    11. Evolving connectivity graphs in mobile phone data p.73 Sanja Brdar

    12. Using Mobile Phone Signalling Data For Estimating Urban Road Traffic States p.76 Thierry Derrmann

    13. Characterizing Significant Places using Temporal Features from Call Detail Records p.79 Mori Kurokawa

    14. Estimating the Indicators on Education and Household Characteristics and Expenditure from Mobile Phone Data in Vanuatu p.82 Jonggun Lee

  • NetMob 5-7 April 2017 Vodafone Village Milan

    POSTERSESSION 1

    5 APRIL 2017

  • 4BOOK OF ABSTRACTS: POSTER 5 -7 April 2017 Vodafone Village Milan

    POSTER SESSION 1

    Churn Prediction in the TelecommunicationIndustry using Social Network Analytics

    Mara Oskarsdottir, Cristian Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens and Jan VanthienenFaculty of Economics and Business, KU Leuven, Naamsestraat 69, 3000 Leuven, Belgium,

    Email: {maria.oskarsdottir, bart.baesens, jan.vanthienen}@kuleuven.beSouthampton Business School, University of Southampton, United Kingdom, Email: [email protected]

    Faculty of Economic and Social Sciences and Solvay Business School, Vrije Universiteit Brussel, Belgium,Email: [email protected]

    Grandata Labs, Argentina, Email: [email protected]

    AbstractIdentifying potential churners is important in com-petitive and saturated markets such as the telecommunicationindustry. Research has shown, that performance of models thatpredict customer churn is increased when social network methodsare applied. Studies also indicate that relational learners, appliedto customer call networks, are capable of predicting churn accu-rately. In this research, the difference in performance of a varietyof relational learners is tested by applying them to a number ofCDR datasets from around the world. In addition, performance ofrelational classifiers and collective inference methods is comparedand the performance of models which combine relational learnerswith mainstream classifying methods, such as logistic regression,is studied. Our results show that collective inference methods donot improve the performance of relational classifiers and thatthe scores of relational learners combined with other customerfeatures result in the best performing models.

    I. INTRODUCTION

    In competitive and saturated markets, such as mobiletelecommunications, identifying potential churners quicklyand effectively is important. Telecommunication providers(telcos) are increasingly making use of network features, inaddition to regular customer features, to capture behaviorand interactions of customers when building churn predictionmodels, as these have been shown to add valuable information[1], [2]. Call detail records (CDR) are aggregated to buildcall networks, from which the network features are extracted.Alternatively, churn propensity can be inferred from call net-works directly by means of relational learners [3], [4]. Thesemethods simulate how churners affect the other customers, bypropagating churn energy through the network. The result isa score for each customer, that can either be used as indicatorof churn directly or to enrich the customer dataset beforebuilding models using classical binary classifiers. In this study,we evaluate the predictive performance of various relationallearners when used on their own and when combined withclassical classifiers. In particular we

    test the difference in performance of a selection of24 relational learners when predicting churn in mobilenetworks,

    study the effect of the two components of relationallearners, relational classifiers and collective inferencemethods, and

    combine relational learners scores with other customerfeatures when building churn prediction models usingbinary classifiers, to determine which sets of featuresprovide the best performance.

    To empirically evaluate the results, the methods were appliedto eight distinct CDR datasets from around the world. Thishigh number of datasets allows us to draw conclusions re-garding the statistical significance of observed differences andprovide conclusive answers.

    II. METHODOLOGYA. Learning in Networks

    Relational learning in network data can be used to clas-sify interlinked nodes in partially labelled networks, as wasdemonstrated in the network learning framework and toolkitNetKit [3]. Assuming that each node can belong to one of nclasses and that the nodes have been partially classified, thatinformation together with structure of the network, such aslinks and weights, can be used to infer classes for the unknownnodes.

    Typically, relational learners are composed of two types ofmethods, namely relational classifiers and collective inferencemethods. Relational classifiers assign a class or score to eachnode in the network, depending on which class the neighboringnodes belong to and the weights of links between them. In thisstudy, we use the weighted vote relational neighbor classifier(wvrn), the class distribution relational neighbor classifier(cdrn), the network-only link-based classifier (nlb) and thespreading activation relational classifier (spaRC), which isthe classifier part of the spreading activation method [4].In contrast, collective inference methods iteratively apply arelational classifier to stabilize the inferencing process [5].Here, weve used Gibbs sampling (gibbs), iterative classifi-cation (ic), relaxation labelling (rl), relaxation labelling withsimulated annealing (rlsa) and spreading activation collectiveinference method (spaCI), which together with spaRC makesup the spreading activation method [4]. We refer to [2] fora description of all these methods. To make up a relationallearner, each relational classifier can be applied on its own orcombined with a collective inference method, which results in24 different methods.

  • 5BOOK OF ABSTRACTS: POSTER 5 -7 April 2017 Vodafone Village Milan

    POSTER SESSION 1

    Fig. 1. The figure shows an example of an application of a relational learner. The figure on the left, displays a graph with seven customers, of which twohave churned (black) and five have not churned (white). The figure on the right shows the same network after the RL has been applied. Each customer nowhas a score or probability of churning.

    Although NetKit can be used for any kind of network,the specific application for customer churn in telco requiressome adjustments [2]. In particular, the relational learners willproduce a score between 0 and 1, since there are only twoclasses, churn (1) and non-churn (0). For churn, prediction aretypically made into the future, where all labels are unknown.The learners were therefore applied to networks at time t,assuming that the churn status of all customers was known, tomake prediction for the following time period t+ 1.

    III. DATASETS AND EXPERIMENTAL SETUP

    TABLE IDESCRIPTIONS OF DATASETS

    ID Origin Year #Customers Churn Rate Sparsity1 Belgium 2010 1.41 106 4.4% 7.931072 Belgium 2010 1.21 106 0.84% 2.201063 North-America 2015 1.5