英国365bet官方

Login to your profile!



No account? sign up!

reptilia-tortoise-network-cs     (Dynamic Networks)

Download network data

This network dataset is in the category of Dynamic Networks







Visualize reptilia-tortoise-network-cs'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
Sourcehttp://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 (November�February)/active (March�October) 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={http://garlandal.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

Nodes73
Edges258
Density0.0981735
Maximum degree35
Minimum degree1
Average degree7
0.525146
Number of triangles2.2K
Average number of triangles29
Maximum number of triangles293
Average clustering coefficient0.366888
Fraction of closed triangles0.498732
Maximum k-core21
Lower bound of Maximum Clique5

Network Data Preview

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

Interactively explore the networks graph structure!

  • Use mouse wheel to zoom in/out
  • Mouseover nodes to see their degree
  • Drag network to see more details

Loading...

Interactive Visualization of Node-level Properties and Statistics

Tools for Interactive Exploration of Node-level Statistics

Visualize and interactively explore reptilia-tortoise-network-cs 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.
Note: You are not logged in!
Please login or join the community to leverage the many other tools and features available in our interactive graph analytics platform.

Interactive Visualization of Node-level Feature Distributions

Node-level Feature Distributions

degree distribution

Loading...

degree CDF

Loading...

degree CCDF

Loading...

kcore distribution

Loading...

kcore CDF

Loading...

kcore CCDF

Loading...

triangle distribution

Loading...

triangle CDF

Loading...

triangle CCDF

Loading...

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.

Discuss and Share

Collaborate and contribute to the first interactive and community-oriented data repository!

Share key insights, awesome visualizations, or simply discuss advantages of data, any observed or known properties, challenges, problems, corrections, and any other helpful comments! Post and discuss recent published works that utilize this dataset (including your own). Any and all feedback is welcome and encouraged.