ia-hospital-ward-proximity (Dynamic Networks)
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This network dataset is in the category of Dynamic Networks
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@inproceedings{nr,
title={The Network Data Repository with Interactive Graph Analytics and Visualization},
author={Ryan A. Rossi and Nesreen K. Ahmed},
booktitle={AAAI},
url={https://networkrepository.com},
year={2015}
}
@Description:This dataset contains the temporal network of contacts between patients,
patients and health-care workers (HCWs) and among HCWs in a hospital ward in Lyon, France, from Monday, December 6, 2010 at 1:00 pm to Friday, December 10, 2010 at 2:00 pm. The study included 46 HCWs and 29 patients.The file contains a tab-separated list representing the active contacts during 20-second intervals of the data collection. Each line has the form “i j t Si Sj“, where i and j are the anonymous IDs of the persons in contact, Si and Sj are their statuses (NUR=paramedical staff, i.e. nurses and nurses’ aides; PAT=Patient; MED=Medical doctor; ADM=administrative staff), and the interval during which this contact was active is [ t – 20s, t ]. If multiple contacts are active in a given interval, you will see multiple lines starting with the same value of t. Time is measured in seconds.1 = NUR/paramedical staff, i.e. nurses and nurses’ aides; 2 = PAT/Patient; 3 = MED/Medical doctor; 4 = ADM/administrative staff,
Network Data Statistics
| Nodes | 75 |
| Edges | 32.4K |
| Density | 11.6843 |
| Maximum degree | 4.3K |
| Minimum degree | 12 |
| Average degree | 864 |
| Assortativity | 0.0862321 |
| Number of triangles | 47M |
| Average number of triangles | 626.5K |
| Maximum number of triangles | 3.8M |
| Average clustering coefficient | 3.64924 |
| Fraction of closed triangles | 0.7538 |
| Maximum k-core | 1.1K |
| Lower bound of Maximum Clique | 72 |
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