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human-Jung2015-M87118219     (Brain Networks)

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This network dataset is in the category of Brain Networks



Visualize bn-human-Jung2015-M87118219'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

CategorySparse networks
CollectionBrain networks
Tags
Sourcehttps://neurodata.io/data/
ShortBrain networks
Edge typeFiber tracts
Edge weightsUnweighted
DescriptionEdges represent fiber tracts that connect one vertex to another.

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.

@article {bigbrain,
     author = {Amunts, Katrin and Lepage, Claude and Borgeat, Louis and Mohlberg, Hartmut and Dickscheid, Timo and Rousseau, Marc-{\'E}tienne and Bludau,
Sebastian and Bazin, Pierre-Louis and Lewis, Lindsay B. and Oros-Peusquens, Ana-Maria and Shah, Nadim J. and Lippert, Thomas and Zilles, Karl and Evans, Alan C.},
     title = {BigBrain: An Ultrahigh-Resolution 3D Human Brain Model},
     volume = {340},
     number = {6139},
     pages = {1472--1475},
     year = {2013},
     publisher = {AAAS},
     journal = {Science}
}

Network Data Statistics

Nodes791.2K
Edges121.9M
Density0.000389465
Maximum degree4.8K
Minimum degree1
Average degree308
Assortativity0.338604
Number of triangles33.9B
Average number of triangles42.8K
Maximum number of triangles1.9M
Average clustering coefficient0.524423
Fraction of closed triangles0.347352
Maximum k-core768
Lower bound of Maximum Clique111

Network Data Preview

Interactive visualization of bn-human-Jung2015_M87118219's graph structure

Interactively explore the networks graph structure!

  • Use mouse wheel to zoom in/out
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Interactive Visualization of Node-level Properties and Statistics

Tools for Interactive Exploration of Node-level Statistics

Visualize and interactively explore bn-human-Jung2015-M87118219 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.
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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.