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mammalia-raccoon-proximity-33     (Animal Social Networks)

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This network dataset is in the category of Animal Social Networks



Visualize mammalia-raccoon-proximity-33's link structure and discover valuable insights using the interactive network data visualization and analytics platform. Compare with hundreds of other network data sets across many different categories and domains.

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, raccoon
Edge typeInteraction
FormatUndirected
Edge weightsWeighted
SpeciesProcyon lotor
Taxon. classMammalia
Populationfree-ranging
Geo. locationIllinois, USA
Data collectionlogger
Interaction typespatial proximity
Definition of interactionClose proximity (within 1Ð1.5m). Any contacts <1 s in duration were excluded.
Edge weight typeduration
Data collection duration7days
Time resolution (within a day)1 sec
Time span (within a day)24 hours
DescriptionNetworks represents adjacency matrices constructed for each week of the year
CitationReynolds, Jennifer JH, et al. "Raccoon contact networks predict seasonal susceptibility to rabies outbreaks and limitations of vaccination." Journal of Animal Ecology 84.6 (2015): 1720-1731.

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={https://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

Nodes13
Edges39
Density0.5
Maximum degree11
Minimum degree2
Average degree6
Assortativity-0.207714
Number of triangles162
Average number of triangles12
Maximum number of triangles26
Average clustering coefficient0.783616
Fraction of closed triangles0.677824
Maximum k-core6
Lower bound of Maximum Clique6

Network Data Preview

Interactive visualization of mammalia-raccoon-proximity-33's graph structure

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Interactive Visualization of Node-level Properties and Statistics

Tools for Interactive Exploration of Node-level Statistics

Visualize and interactively explore mammalia-raccoon-proximity-33 and its important node-level statistics!

  • 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
  • Zoom in/out on the visualization you created at any point by using the buttons below on the left.
  • 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.
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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.