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fish-stickleback-group-6     (Animal Social Networks)

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This network dataset is in the category of Animal Social Networks



Visualize fish-stickleback-group-6'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, Fish, fishstickleback
Edge typeInteraction
FormatUndirected
Edge weightsWeighted
SpeciesGasterosteus aculeatus
Taxon. classActinopterygii
Populationcaptive
Geo. locationSt. Andrews, UK
Data collectionvideo
Interaction typegroup membership
Definition of interactionA pair of individuals were classed as shoaling if they were within four body lengths of one another from head to head. Gambit of the group approach was then used to assume that a string of fish connected by less than two body lengths were all assumed to be associating with one another.
Edge weight typefrequency
Data collection duration120 min
Time resolution (within a day)6 min
Time span (within a day)120 min
DescriptionNetworks represent social data collected from seven replicate groups of fish
CitationAtton, N., et al. "Familiarity affects social network structure and discovery of prey patch locations in foraging stickleback shoals." Proceedings of the Royal Society of London B: Biological Sciences 281.1789 (2014): 20140579.

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

Nodes10
Edges45
Density1
Maximum degree9
Minimum degree9
Average degree9
Assortativitynan
Number of triangles360
Average number of triangles36
Maximum number of triangles36
Average clustering coefficient1
Fraction of closed triangles1
Maximum k-core10
Lower bound of Maximum Clique10

Network Data Preview

Interactive visualization of fish-stickleback-group-6'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 fish-stickleback-group-6 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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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.