# Predicting complex user behavior from CDR based social networks

## TL;DR

This paper demonstrates how to construct social networks from noisy CDR data and use them to predict user defaults on payments by analyzing various network-derived features.

## Contribution

It introduces a methodology for building complex behavior prediction models using large, diverse feature sets from social networks derived from CDR data, with systematic feature selection.

## Key findings

- Features based on close ties outperform geographical features.
- Number of calls cutoff significantly affects network properties.
- Large feature sets improve prediction accuracy.

## Abstract

Call Detail Record (CDR) datasets provide enough information about personal interactions to support building and analyzing detailed empirical social networks. We take one such dataset and describe the various ways of using it to create a true social network in spite of the highly noisy data source. We use the resulting network to predict each individual's likelihood to default on payments for the network services, a complex behavior that involves a combination of social, economic, and legal considerations. We use a large number of features extracted from the network to build a model for predicting which users will default. By analyzing the relative contributions of features, we choose their best performing subsets ranging in size from small to medium. Features based on the number of close ties maintained by a user performed better than those derived from user's geographical location. The paper contributions include systematic impact analysis that the number of calls cutoff has on the properties of the network derived from CDR, and a methodology for building complex behavior models by creating very large sets of diverse features and systematically choosing those which perform best for the final model.