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mammalia-voles-rob-trapping     (Dynamic Networks)

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This network dataset is in the category of Dynamic Networks







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Metadata

CategoryAnimal Social Networks
CollectionAnimal Networks
AboutReal-world animal interaction network data sets. Animal interaction data from published studies of wild, captive, and domesticated animals.
Tags
Sourcehttp://bansallab.github.io/asnr/data.html
ShortAnimal Networks
Vertex typeAnimal, Mammal, voles
Edge typeInteraction
FormatUndirected
Edge weightsWeighted
SpeciesMicrotus agrestis
Taxon. classMammalia
Populationfree-ranging
Geo. locationNorthumberland, England
Data collectionmark recapture
Interaction typesocial projection bipartite
Definition of interactionAn 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 typefrequency
Data collection duration6 days
Time resolution (within a day)12 hours
Time span (within a day)24 hours
DescriptionNetworks 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.
CitationDavis, Stephen, et al. "Spatial analyses of wildlife contact networks." Journal of the Royal Society Interface 12.102 (2015): 20141004.
Edge timestampsThird 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:

@inproceedings{nr,
     title={The Network Data Repository with Interactive Graph Analytics and Visualization},
     author={Ryan A. Rossi and Nesreen K. Ahmed},
     booktitle={AAAI},
     url={http://garlandal.com},
     year={2015}
}

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.

Network Data Statistics

Nodes1.5K
Edges4.6K
Density0.00417466
Maximum degree39
Minimum degree1
Average degree6
0.07735
Number of triangles14.1K
Average number of triangles9
Maximum number of triangles108
Average clustering coefficient0.522543
Fraction of closed triangles0.304632
Maximum k-core10
Lower bound of Maximum Clique6

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Interactive Visualization of Node-level Feature Distributions

Node-level Feature Distributions

degree distribution

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degree CDF

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degree CCDF

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kcore distribution

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kcore CDF

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kcore CCDF

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triangle distribution

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triangle CDF

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triangle CCDF

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All visualizations and analytics are interactive and flexible for exploratory analysis and data mining in real-time and include the following features:

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