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reptilia-tortoise-network-fi     (Dynamic Networks)

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This network dataset is in the category of Dynamic 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.
Edge timestampsThird column encodes the weights for the edges and the fourth column represents the edge timestamps. If the graph is unweighted (has only 3 columns), then the third column represents the timestamps.For this temporal network, edge timestamps are not recorded at the finest granularity (sec. or ms.) and are instead discrete approximations of the actual temporal network. Unfortunately, the actual edge timestamps, that is, when the interactions were actually observed (e.g., at the level of seconds) has not been provided.Hence, one can create a sequence of static snapshot graphs by aggregating all edges that occur at each unique edge timestamp and repeating this for all edge timestamps.

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

Nodes787
Edges1.7K
Density0.00553847
Maximum degree40
Minimum degree1
Average degree4
Assortativity0.53928
Number of triangles7.6K
Average number of triangles9
Maximum number of triangles266
Average clustering coefficient0.353495
Fraction of closed triangles0.463881
Maximum k-core16
Lower bound of Maximum Clique5

Network Data Preview

Interactive visualization of reptilia-tortoise-network-fi's graph structure

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Interactive Visualization of Node-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:

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