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 similar to 87%, which outperforms by similar to 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-way indirect inference method based
on observed homophilic structural correlations. Our results have straightforward applications
in behavioural prediction and personal marketing.
