Harnessing Mobile Phone Social Network Topology to Infer Users Demographic Attributes

TL;DR

This paper analyzes mobile phone social network topology in Mexico to predict users' age with high accuracy, leveraging network structure and known user ages to infer demographics at scale.

Contribution

It introduces a novel graph-based algorithm that uses network topology and seed data to accurately predict users' age categories in a large mobile phone network.

Findings

Achieved 62% accuracy in 4-category age prediction, surpassing random guess baseline of 25%.

Enhanced prediction accuracy to 72% using probabilistic inference methods.

Demonstrated the effectiveness of topological metrics in improving demographic inference.

Abstract

We study the structure of the social graph of mobile phone users in the country of Mexico, with a focus on demographic attributes of the users (more specifically the users' age). We examine assortativity patterns in the graph, and observe a strong age homophily in the communications preferences. We propose a graph based algorithm for the prediction of the age of mobile phone users. The algorithm exploits the topology of the mobile phone network, together with a subset of known users ages (seeds), to infer the age of remaining users. We provide the details of the methodology, and show experimental results on a network GT with more than 70 million users. By carefully examining the topological relations of the seeds to the rest of the nodes in GT, we find topological metrics which have a direct influence on the performance of the algorithm. In particular we characterize subsets of users for which the accuracy of the algorithm is 62% when predicting between 4 age categories (whereas a pure random guess would yield an accuracy of 25%). We also show that we can use the probabilistic information computed by the algorithm to further increase its inference power to 72% on a significant subset of users.