# Social Events in a Time-Varying Mobile Phone Graph

## TL;DR

This paper explores how mobile phone data can be used to detect large social events by analyzing antenna activity and social relationships, and assesses the potential to infer attendance of unobserved users.

## Contribution

It introduces methods to detect social events from mobile data, characterize social cohesion, and infer unobserved user presence, integrating activity and network analysis.

## Key findings

- Large social events show increased antenna activity and social relationships.
- Detected events can be characterized by social cohesion metrics.
- Unobserved user presence can be inferred from network connections.

## Abstract

The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large social events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users were in the event.