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Interaction data from the Copenhagen Networks Study.

Piotr Sapiezynski¹, Arkadiusz Stopczynski¹, David Dreyer Lassen²

¹DTU Compute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.

Scientific Data

|December 13, 2019

Summary

This summary is machine-generated.

This study introduces a unique multi-layer temporal network dataset of over 700 university students, capturing physical proximity and communication patterns over four weeks for advanced social network analysis.

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Area of Science:

Social Network Analysis
Computational Social Science
Network Science

Background:

Understanding human social dynamics requires detailed network data.
Previous datasets often lack multi-modal communication or high temporal resolution.

Purpose of the Study:

To introduce a novel, multi-layer temporal network dataset.
To facilitate research in human social network analysis and modeling.
To provide a rich resource for studying complex social interactions.

Main Methods:

Data collected from over 700 university students using smartphones.
Four weeks of data collection including physical proximity (Bluetooth), phone calls, text messages, and Facebook friendships.
High temporal resolution tracking of multiple network layers.

Main Results:

A unique dataset comprising diverse communication and proximity networks.
The dataset captures interactions within a large population over an extended period.
Provides granular data on social connections and communication patterns.

Conclusions:

The dataset is a valuable resource for advancing the analysis and modeling of human social networks.
Enables novel research into the structure and dynamics of complex social systems.
Facilitates interdisciplinary research in sociology, computer science, and network theory.