  Patterns of mobile phone communications, coupled with the information of the
social network graph and financial behavior, allow us to make inferences of
users' socio-economic attributes such as their income level. We present here
several methods to extract features from mobile phone usage (calls and
messages), and compare different combinations of supervised machine learning
techniques and sets of features used as input for the inference of users'
income. Our experimental results show that the Bayesian method based on the
communication graph outperforms standard machine learning algorithms using
node-based features.

Computers and Society;Machine Learning;Social and Information Networks;Machine Learning