Abstract
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.
Highlights
The availability of population-level communication data has made it possible to study human interaction patterns on very large scales [1,2,3,4]
Since our goal is to examine the variation in calling and texting patterns among pairs, we limit ourselves to information that can be obtained from the call detail records of two users in a pair
The principal components analysis (PCA) estimates these loadings with the eigenvectors scaled by the variance explained, and these loadings can be rotated for ease of interpretation; details are given in Ref. [39]
Summary
The availability of population-level communication data has made it possible to study human interaction patterns on very large scales [1,2,3,4]. The utilization of mobile phone usage data along with anonymized demographic information of users has proven to be extremely effective in understanding the age and gender dependence [5,6,7,8,9,10,11,12], as well as the innate behavioral traits [13,14,15]. The general network-based research approach has been furthered by the inclusion of machine learning to exploit the differences in social interaction found in different age and gender types and to predict demographic information of individuals. De Montjoye et al [16] predicted personality traits of users based.
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