Link-centric analysis of variation by demographics in mobile phone communication patterns

Autoři: Mikaela Irene D. Fudolig aff001;  Kunal Bhattacharya aff002;  Daniel Monsivais aff003;  Hang-Hyun Jo aff001;  Kimmo Kaski aff003
Působiště autorů: Asia Pacific Center for Theoretical Physics, Pohang, Republic of Korea aff001;  Department of Industrial Engineering and Management, Aalto University School of Science, Espoo, Finland aff002;  Department of Computer Science, Aalto University School of Science, Espoo, Finland aff003;  Department of Physics, Pohang University of Science and Technology, Pohang, Republic of Korea aff004;  The Alan Turing Institute, London, England, United Kingdom aff005
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227037


We present a link-centric approach to study variation in the mobile phone communication patterns of individuals. Unlike most previous research on call detail records that focused on the variation of phone usage across individual users, we examine how the calling and texting patterns obtained from call detail records vary among pairs of users and how these patterns are affected by the nature of relationships between users. To demonstrate this link-centric perspective, we extract factors that contribute to the variation in the mobile phone communication patterns and predict demographics-related quantities for pairs of users. The time of day and the channel of communication (calls or texts) are found to explain most of the variance among pairs that frequently call each other. Furthermore, we find that this variation can be used to predict the relationship between the pairs of users, as inferred from their age and gender, as well as the age of the younger user in a pair. From the classifier performance across different age and gender groups as well as the inherent class overlap suggested by the estimate of the bounds of the Bayes error, we gain insights into the similarity and differences of communication patterns across different relationships.

Klíčová slova:

Age groups – Behavior – Cell phones – Communications – Machine learning – principal component analysis – Social communication – Forecasting


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Článek vyšel v časopise


2020 Číslo 1