Measuring Large-Scale Social Networks with High Resolution

By Arkadiusz Stopczynski, Vedran Sekara, Piotr Sapiezynski, Andrea Cuttone, Jakob Eg Larsen, Sune Lehmann. PLoS ONE, 2014

398 citations recorded by the source corpus.

This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years—the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/87efbe8a3f1d87d08b49abc9cbcfae91d81c25bd

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Citations in the corpus

Statistical physics of vaccination (2016, background): “At this time-scale the distribution of number of contacts does not show large heterogeneities [168, 395, 642, 676, 684, 713].”
A Survey on Deep Learning for Human Mobility (2020, background): “The US Global Positioning System (GPS) is the most popular one, and GPS receivers are ubiquitous in many tools of everyday life, such as mobile phones [7, 183, 238], vehicles [64, 137, 140], vessels [161, 173, 196], and wearable devices [157].”
Academic performance prediction in a gender-imbalanced environment (2017, methodology): “The data used in this paper was collected as part of the Copenhagen Networks Study (CNS), a large scale computational social science study designed to measure human interactions and mobility with high resolution [20].”
Data ex Machina: Introduction to Big Data (2017, background): “Further, these ethical issues in turn suggest interesting researchable questions, ranging from issues around reidentification (Sweeney 2002) to the meaning and management of consent by subjects (Stopczynski et al. 2014a,b).”
COVID-19 lockdown induces disease-mitigating structural changes in mobility networks (2020, background): “Mirroring real systems, we can assume that this inequality holds when averaged over a single day, because (i) most individuals are isolated at night and (ii) many individuals are not in contact with other people for the majority of the day (14, 15).”