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reptilia-tortoise-network-cs-2011-inact     (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, Reptile, desert, tortoise
Edge typeInteraction
FormatUndirected
Edge weightsUnweighted
SpeciesGopherus agassizii
Taxon. classReptilia
Populationfree-ranging
Geo. locationNevada, USA
Data collectionradio tags
Interaction typesocial projection bipartite
Definition of interactionA bipartite network was first constructed based on burrow use - an edge connecting a tortoise node to a burrow node indicated burrow use by the individual. Social networks of desert tortoises were then constructed by the bipartite network into a single-mode projection of tortoise nodes.
Edge weight typeunweighted
Data collection duration8 months
Time span (within a day)focal follow/ad libitum
DescriptionNetworks represent social data collected over different years and inactive (NovemberÐFebruary)/active (MarchÐOctober) season.
CitationSah, Pratha, et al. "Inferring social structure and its drivers from refuge use in the desert tortoise, a relatively solitary species." Behavioral Ecology and Sociobiology 70.8 (2016): 1277-1289.

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

Nodes10
Edges8
Density0.177778
Maximum degree2
Minimum degree1
Average degree1
Assortativity1
Number of triangles6
Average number of triangles0
Maximum number of triangles1
Average clustering coefficient0.6
Fraction of closed triangles1
Maximum k-core3
Lower bound of Maximum Clique3

Network Data Preview

Interactive visualization of reptilia-tortoise-network-cs-2011-inact'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 reptilia-tortoise-network-cs-2011-inact 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
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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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