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sp_office — Workplace contacts (2013)

Description

Temporal network of face-to-face proximity contacts among 92 employees in an office building of the French Institute for Public Health Surveillance (InVS) in Saint-Maurice, France, from 24 June to 3 July 2013, measured by the SocioPatterns collaboration with wearable sensors. Nodes are employees, and each edge is a contact between two employees active during a 20-second interval of the data collection, so that a pair of employees is connected by one edge per interval in which they were in contact.

The network has the following properties:

  • (Node) id: the anonymous identifier of the employee.
  • (Node) department: the department of the employee (DISQ, DMCT, DSE, SFLE or SRH).
  • (Edge) time: the end of the 20-second interval $[t - 20\,\text{s}, t]$ during which the contact was active, in seconds since 0:00 on 24 June 2013.
Tags
Social Offline Weighted Temporal Metadata
Added
2020-08-11
Citation
  • M. Génois et al., "Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers." Network Science 3(3), 326-347 (2015) 10.1017/nws.2015.10 Sci-Hub
Upstream URL OK
https://sociopatterns.org/datasets/contacts-in-a-workplace/
Upstream license
Creative Commons CC0 1.0 Universal (Public Domain Dedication), https://creativecommons.org/publicdomain/zero/1.0/
Networks
Tip: hover over a column header for a description.
Column legend
$\left<k\right>$
Mean degree
$\sigma_k$
Standard deviation of the degrees
$\lambda_h$
Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$
Random walk mixing time
$r$
Degree assortativity
$c$
Global clustering coefficient
$\oslash$
(Pseudo-) diameter
$S$
Relative size of the largest component
NPs
Node properties
EPs
Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
sp_office 92 9,827 213.63 194.76 18.72 19.35 0.50 0.46 3 1.00 Undirected Unipartite id department time 21 KiB 35 KiB 32 KiB 28 KiB
Ridiculograms*
* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.