Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology

Abstract

Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper, we focus on the population of Mexican mobile phone users. We first present an observational study of mobile phone usage according to gender and age groups. We are able to detect significant differences in phone usage among different subgroups of the population. We then study the performance of different machine learning (ML) methods to predict demographic features (namely, age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We show how a specific implementation of a diffusion model, harnessing the graph structure, has significantly better performance over other node-based standard ML methods. We provide details of the methodology together with an analysis of the robustness of our results to changes in the model parameters. Furthermore, by carefully examining the topological relations of the training nodes (seed nodes) to the rest of the nodes in the network, we find topological metrics which have a direct influence on the performance of the algorithm.

arXiv commentarXiv admin note: substantial text overlap with arXiv:1511.06656, arXiv:1511.07337

References in corpus (12)

• Scikit-learn: Machine Learning in Python
• Understanding individual human mobility patterns
• Multilayer Networks
• Structure and tie strengths in mobile communication networks
• The Anatomy of the Facebook Social Graph
• Understanding the spreading patterns of mobile phone viruses
• The Matthew effect in empirical data
• A Study of Age and Gender seen through Mobile Phone Usage Patterns in Mexico
• Evaluating socio-economic state of a country analyzing airtime credit and mobile phone datasets
• Empirical stationary correlations for semi-supervised learning on graphs
• Time allocation in social networks: correlation between social structure and human communication dynamics
• Human Mobility and Predictability enriched by Social Phenomena Information

Cited by in corpus (3)

• Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecture
• Link-centric analysis of variation by demographics in mobile phone communication patterns
• Snel: SQL Native Execution for LLVM