We present a link-centric approach to study variation in the mobile phone communication patterns of users. 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. By looking at the classifier performance across different age and gender groups, we gain insights into how communication patterns vary across different relationships.August 1, 2019 1/19 networks [23,24] have also been adopted. These studies have mainly relied on the egocentric perspective, working on the implicit assumption that an individual's traits determine his or her calling and texting patterns. Thus, when using an egocentric approach in predicting individual user information, all calls and texts made by the target individual are aggregated over all its neighbors, so the links in the target individual's egocentric network are not differentiated from each other. While the communication patterns have been shown to vary with age and gender at the individual level as discussed above, the communication patterns between two individuals have also been found to vary with the relationship between them [25,26]. However, while an egocentric perspective works well in studying variation at the individual level, it cannot, by design, provide much insight into variation among pairs. We hope to expand knowledge in this area by focusing on the links rather than the nodes in the mobile phone communication network, analyzing each link independently from others instead of aggregating them over the egocentric network. Such link-centric approach has not been explored much in relation to mobile phone data [27,28], especially in prediction tasks. In particular, only Herrera et al. [23] have used isolated link information as well as the demographic information of one user in the link to predict the age and gender of the other user. Still, as their aim was to predict individual user information, they did not touch on the connection between the communication patterns and the relationships between the pairs of users.It is important to note that while the age and gender can be categorically confirmed from the datasets, determining the relationships between any pair of users from the mobile phone data relies on inference. In particular, unless otherwise specified, we mean such in...

No abstract

We analyze a large-scale mobile phone call dataset containing information on the age, gender, and billing locality of users to get insight into social closeness in pairs of individuals of similar age. We show that in addition to using the demographic information, the ranking of contacts by their call frequency in egocentric networks is crucial to characterize the different communication patterns. We find that mutually top-ranked opposite-gender pairs show the highest levels of call frequency and daily regularity, which is consistent with the behavior of real-life romantic partners. At somewhat lower level of call frequency and daily regularity come the mutually topranked same-gender pairs, while the lowest call frequency and daily regularity are observed for mutually non-top-ranked pairs. We have also observed that older pairs tend to call less frequently and less regularly than younger pairs, while the average call durations exhibit a more complex dependence on age. We expect that a more detailed analysis can help us better characterize the nature of relationships between pairs of individuals and distinguish between various types of relations, such as siblings, friends, and romantic partners.

Using large-scale call detail records of anonymised mobile phone service subscribers with demographic and location information, we investigate how a long-distance residential move within the country affects the mobile communication patterns between an ego who moved and a frequently called alter who did not move. By using clustering methods in analysing the call frequency time series, we find that such ego-alter pairs are grouped into two clusters, those with the call frequency increasing and those with the call frequency decreasing after the move of the ego. This indicates that such residential moves are correlated with a change in the communication pattern soon after moving. We find that the pre-move calling behaviour is a relevant predictor for the post-move calling behaviour. While demographic and location information can help in predicting whether the call frequency will rise or decay, they are not relevant in predicting the actual call frequency volume. We also note that at four months after the move, most of these close pairs maintain contact, even if the call frequency is decreased.