2017/06/20 by Carlos Sarraute, Jorge Brea, Sarraute, Carlos +10
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Physical sciences #Human Mobility and Location-Based Analysis #Opportunistic and Delay-Tolerant Networks #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.CY #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1706.06253
Published in NetMob 2015 (Fourth Conference on the Scientific Analysis of Mobile Phone Datasets), MIT Media Lab, Cambridge, USA, 8-10 April 2015
arxiv created 2017/06/20 · openalex publication_date 2017/06/20 · arxiv updated 2017/06/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.