Data analysis and call prediction on dyadic data from an understudied population

Nasim, Mehwish; Rextin, Aimal; Hayat, Shamaila; Khan, Numair; Malik, Muhammad Muddassir

Data analysis and call prediction on dyadic data from an understudied population

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2017

Autor:innen

Nasim, Mehwish

Rextin, Aimal

Hayat, Shamaila

Khan, Numair

Malik, Muhammad Muddassir

DOI (zitierfähiger Link)

https://doi.org/10.1016/j.pmcj.2017.08.002

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Pervasive and Mobile Computing. 2017, 41, pp. 166-178. ISSN 1574-1192. eISSN 1873-1589. Available under: doi: 10.1016/j.pmcj.2017.08.002

Zusammenfassung

In this paper we predict outgoing mobile phone calls using machine learning and time clusters based approaches. We analyze to which extent the calling activity of mobile phone users is predictable. The premise is that mobile phone users exhibit temporal regularity in their interactions with majority of their contacts. In the sociological context, most social interactions have fairly reliable temporal regularity. If we quantify the extension of this behavior to interactions on mobile phones we expect that pairwise interaction is not merely a result of randomness, rather it exhibits a temporal pattern. To this end, we not only tested our approach on an original mobile phone usage dataset from a developing country, Pakistan, but we also analyzed the famous Reality Mining Dataset and the Nokia Dataset (from a European country), where we found an equitable basis for comparison with our data. Our original data consists of 783 users and more than 12,000 active dyads. Our results show that temporal information about pairwise user interactions can predict future calls with reasonable accuracy.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

Smartphone, Call-logs, Call prediction, Temporal regularity

Zitieren

ISO 690

NASIM, Mehwish, Aimal REXTIN, Shamaila HAYAT, Numair KHAN, Muhammad Muddassir MALIK, 2017. Data analysis and call prediction on dyadic data from an understudied population. In: Pervasive and Mobile Computing. 2017, 41, pp. 166-178. ISSN 1574-1192. eISSN 1873-1589. Available under: doi: 10.1016/j.pmcj.2017.08.002

BibTex

@article{Nasim2017-10analy-40818,
  year={2017},
  doi={10.1016/j.pmcj.2017.08.002},
  title={Data analysis and call prediction on dyadic data from an understudied population},
  volume={41},
  issn={1574-1192},
  journal={Pervasive and Mobile Computing},
  pages={166--178},
  author={Nasim, Mehwish and Rextin, Aimal and Hayat, Shamaila and Khan, Numair and Malik, Muhammad Muddassir}
}

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