# Measuring large-scale social networks with high resolution

arXiv: https://arxiv.org/abs/1401.7233 · HTML: https://mlsanity.com/paper/1401.7233/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2014.

## Built on by (most cited first, 7 of 7)

- [An embedding-based distance for temporal graphs](https://mlsanity.com/paper/2401.12843/) (2024)
- [Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis](https://mlsanity.com/paper/2208.01365/) (2022)
- [Recovering lost and absent information in temporal networks](https://mlsanity.com/paper/2107.10835/) (2021)
- [DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series](https://mlsanity.com/paper/2203.15009/) (2022)
- [Filtration-Based Representation Learning for Temporal Graphs](https://mlsanity.com/paper/2502.10076/) (2025)
- [Graph Neural Networks for Source Detection: A Review and Benchmark Study](https://mlsanity.com/paper/2512.20657/) (2025)
- [A temporal graph dissimilarity measure capturing epidemic spreading behavior](https://mlsanity.com/paper/2610.16456/) (2026)

## Abstract (arXiv)

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, demographic, health, politics) for a densely connected population of 1,000 individuals, using state-of-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.