  In this paper we investigate the behavioural differences between mobile phone
customers with prepaid and postpaid subscriptions. Our study reveals that (a)
postpaid customers are more active in terms of service usage and (b) there are
strong structural correlations in the mobile phone call network as connections
between customers of the same subscription type are much more frequent than
those between customers of different subscription types. Based on these
observations we provide methods to detect the subscription type of customers by
using information about their personal call statistics, and also their
egocentric networks simultaneously. The key of our first approach is to cast
this classification problem as a problem of graph labelling, which can be
solved by max-flow min-cut algorithms. Our experiments show that, by using both
user attributes and relationships, the proposed graph labelling approach is
able to achieve a classification accuracy of $\sim 87\%$, which outperforms by
$\sim 7\%$ supervised learning methods using only user attributes. In our
second problem we aim to infer the subscription type of customers of external
operators. We propose via approximate methods to solve this problem by using
node attributes, and a two-ways indirect inference method based on observed
homophilic structural correlations. Our results have straightforward
applications in behavioural prediction and personal marketing.
