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mammalia-zebra-interaction     (Animal Social Networks)

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This network dataset is in the category of Animal Social Networks



Visualize mammalia-zebra-interaction'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, zebra, groupmembership
Edge typeInteraction
FormatUndirected
Edge weightsWeighted
SpeciesEquus grevyi
Taxon. classMammalia
Populationfree-ranging
Geo. locationKenya
Data collectionsurvey scan
Interaction typegroup membership
Definition of interactionA group was defined as a set of one or more individuals that is spatially cohesive and distinct from other groups at the time of observation. Edges were constructed based on half-weight index (HWI).
Edge weight typeunweighted
Data collection duration3 months
Time span (within a day)focal follow/ad libitum
CitationSiva R Sundaresan, Ilya R Fischhoff, Jonathan Dushoff, and Daniel I Rubenstein. Network metrics reveal differences in social organization between two fission-fusion species, Grevy's zebra and onager. Oecologia, 151(1):140-149, 2007.

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

Nodes27
Edges111
Density0.316239
Maximum degree14
Minimum degree2
Average degree8
Assortativity0.717703
Number of triangles936
Average number of triangles34
Maximum number of triangles72
Average clustering coefficient0.875914
Fraction of closed triangles0.844765
Maximum k-core12
Lower bound of Maximum Clique10

Network Data Preview

Interactive visualization of mammalia-zebra-interaction'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-zebra-interaction 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.
  • 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.