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insecta-ant-trophallaxis-colony1-day3     (Animal Social Networks)

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This network dataset is in the category of Animal Social Networks



Visualize insecta-ant-trophallaxis-colony1-day3'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, Insect, ants, trophallaxis
Edge typeInteraction
FormatUndirected
Edge weightsWeighted
SpeciesCamponotus pennsylvanicus
Taxon. classInsecta
Populationcaptive
Geo. locationOld Main, State College, Pennsylvania
Data collectionvideo
Interaction typetrophallaxis
Definition of interactionA trophallaxis event was recorded when ants engaged in mandible-to-mandible contact for greater than 1 s
Edge weight typeduration
Data collection duration1 day
Time resolution (within a day)1sec
Time span (within a day)20min
DescriptionNetworks represent two C. pennsylvanicus colonies. Each colony was filmed for approximately 30 minutes for 8 consecutive nights.
CitationQuevillon, Lauren E., et al. "Social, spatial, and temporal organization in a complex insect society." Scientific reports 5 (2015).

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

Nodes29
Edges28
Density0.0689655
Maximum degree6
Minimum degree1
Average degree1
Assortativity0.119205
Number of triangles3
Average number of triangles0
Maximum number of triangles1
Average clustering coefficient0.0195402
Fraction of closed triangles0.0625
Maximum k-core3
Lower bound of Maximum Clique3

Network Data Preview

Interactive visualization of insecta-ant-trophallaxis-colony1-day3'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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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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