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mammalia-voles-kcs-trapping-41     (Animal Social Networks)

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This network dataset is in the category of Animal Social 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
Sourcehttps://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.

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://networkrepository.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

Nodes34
Edges2
Density0.00356506
Maximum degree1
Minimum degree0
Average degree0
Assortativitynan
Number of triangles0
Average number of triangles0
Maximum number of triangles0
Average clustering coefficient0
Fraction of closed trianglesnan
Maximum k-core2
Lower bound of Maximum Clique2

Network Data Preview

Interactive visualization of mammalia-voles-kcs-trapping-41's graph structure

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Interactive Visualization of Node-level Properties and Statistics

Tools for Interactive Exploration of Node-level Statistics

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  • Each point represents a node (vertex) in the graph.
  • A subset of interesting nodes may be selected and their properties may be visualized across all node-level statistics. To select a subset of nodes, hold down the left mouse button while dragging the mouse in any direction until the nodes of interest are highlighted.This feature allows users to explore and analyze various subsets of nodes and their important interesting statistics and properties to gain insights into the graph data
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  • Once a subset of interesting nodes are selected, the user may further analyze by selecting and drilling down on any of the interesting properties using the left menu below.
  • We also have tools for interactively visualizing, comparing, and exploring the graph-level properties and statistics.
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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:

  • Degree, k-core, triangles, and triangle-core distributions. We include plots for each of the fundamental graph features and counts of the number with a particular property (i.e., number of nodes that form k triangles or have degree k, etc.)
  • We also include the CDF and CCDF distributions for each graph in the collection.
  • All visualizations and plots are zoomable. One may zoom-in or out on the data visualization using scrolling.
  • Panning. Users may also click anywhere on the plot and move the mouse in any direction to pan.
  • Adjust scale and other application dependent-parameters. All interactive visualizations may adjust the scale which is particularly important in certain types of graph data that contain highly skewed graph properties (power-lawed graphs and/or networks) such as degree distribution.

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