mammalia-voles-plj-trapping (Dynamic Networks)
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This network dataset is in the category of Dynamic Networks
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Metadata
| Category | Animal Social Networks |
| Collection | Animal Networks |
| About | Real-world animal interaction network data sets. Animal interaction data from published studies of wild, captive, and domesticated animals. |
| Tags | |
| Source | https://bansallab.github.io/asnr/data.html |
| Short | Animal Networks |
| Vertex type | Animal, Mammal, voles |
| Edge type | Interaction |
| Format | Undirected |
| Edge weights | Weighted |
| Species | Microtus agrestis |
| Taxon. class | Mammalia |
| Population | free-ranging |
| Geo. location | Northumberland, England |
| Data collection | mark recapture |
| Interaction type | social projection bipartite |
| Definition of interaction | An edge was inserted into the network whenever two voles were caught in at least one common trap over the primary trapping sessions being considered |
| Edge weight type | frequency |
| Data collection duration | 6 days |
| Time resolution (within a day) | 12 hours |
| Time span (within a day) | 24 hours |
| Description | Networks represent social data combined over two consecutive trapping sessions at four sites (BHP, KCS, PLJ and ROB). Populations were trapped in "primary" sessions every 28 days from March to November, and every 56 days from November to March. |
| Citation | Davis, Stephen, et al. "Spatial analyses of wildlife contact networks." Journal of the Royal Society Interface 12.102 (2015): 20141004. |
| Edge timestamps | Third column encodes the weights for the edges and the fourth column represents the edge timestamps. If the graph is unweighted (has only 3 columns), then the third column represents the timestamps.For this temporal network, edge timestamps are not recorded at the finest granularity (sec. or ms.) and are instead discrete approximations of the actual temporal network. Unfortunately, the actual edge timestamps, that is, when the interactions were actually observed (e.g., at the level of seconds) has not been provided.Hence, one can create a sequence of static snapshot graphs by aggregating all edges that occur at each unique edge timestamp and repeating this for all edge timestamps. |
Please cite the following if you use the data:
Note that if you transform/preprocess the data, please consider sharing the data by uploading it along with the details on the transformation and reference to any published materials using it.
@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}
}
Network Data Statistics
| Nodes | 1.3K |
| Edges | 3.9K |
| Density | 0.00484721 |
| Maximum degree | 49 |
| Minimum degree | 1 |
| Average degree | 6 |
| Assortativity | 0.306193 |
| Number of triangles | 15.6K |
| Average number of triangles | 12 |
| Maximum number of triangles | 310 |
| Average clustering coefficient | 0.507314 |
| Fraction of closed triangles | 0.370704 |
| Maximum k-core | 13 |
| Lower bound of Maximum Clique | 6 |
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