sp_colocation — Social co-locations (2018)
- Description
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window. Nodes are individuals, and each edge connects two individuals in co-presence during one 20s time window (given by the edge property
time), so that a pair of individuals is connected by one edge per time window in which they were in co-presence.We release here 6 co-presence data sets. The contexts in which these data were collected are: a workplace, with data collected in two different years (InVS13, InVS15), a hospital (LH10), a primary school (LyonSchool), a scientific conference (SFHH) and a high school (Thiers13). In each case, the data on face-to-face contacts during the same timeframe has already been released. In all cases except SFHH, metadata is also available concerning the department, class or status of each individual.
- Tags
- Social Offline Unweighted Weighted Temporal Metadata
- Added
- 2021-08-07
- Citation
-
- Génois, M., Barrat, A. Can co-location be used as a proxy for face-to-face contacts?. EPJ Data Sci. 7, 11 (2018) 10.1140/epjds/s13688-018-0140-1 Sci-Hub
- Upstream URL OK
- https://sociopatterns.org/datasets/co-location-data-for-several-sociopatterns-data-sets/
- Upstream license
- Creative Commons CC0 1.0 Universal (Public Domain Dedication), https://creativecommons.org/publicdomain/zero/1.0/
- Networks
-
Tip: click on a column header to sort the list, or hover over it 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 Thiers13 332 18,613,039 112126.74 42531.96 268.38 41.01 0.42 0.98 2 0.99 Undirected Unipartite id group time 5.2 MiB 54.7 MiB 49.8 MiB 44.2 MiB SFHH 403 1,417,485 7034.67 4468.01 370.80 0.89 0.11 0.97 2 1.00 Undirected Unipartite id group time 998 KiB 4.3 MiB 3.9 MiB 3.6 MiB LyonSchool 242 6,594,492 54499.93 20291.95 220.54 2.47 0.20 0.97 2 1.00 Undirected Unipartite id group time 1.8 MiB 18.8 MiB 17.1 MiB 14.9 MiB LH10 81 150,126 3706.81 3408.10 43.50 14.87 0.32 0.77 3 0.90 Undirected Unipartite id group time 143 KiB 417 KiB 390 KiB 371 KiB InVS15 232 1,283,194 11062.02 7547.35 160.74 2.83 0.26 0.88 3 0.94 Undirected Unipartite id group time 919 KiB 4.0 MiB 3.6 MiB 3.1 MiB InVS13 100 394,247 7884.94 5777.65 83.70 4.19 0.26 0.90 2 0.95 Undirected Unipartite id group time 341 KiB 1.1 MiB 1.0 MiB 969 KiB - Ridiculograms*