Login to your profile!



No account? sign up!

kron-g500-logn21     (DIMACS10)

Download network data

This network dataset is in the category of DIMACS10



Visualize kron-g500-logn21'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

Tags
AuthorD. Bader, J. Berry, S. Kahan, R. Murphy, E. Reidy, J. Willcock
Date2010
Edge weightsWeighted
Metadataundirected multigraph
DescriptionDIMACS10 set

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

Nodes2.1M
Edges91M
Density4.14006e-05
Maximum degree213.9K
Minimum degree0
Average degree86
Assortativity-0.30056
Number of triangles26.4B
Average number of triangles12.6K
Maximum number of triangles59.2M
Average clustering coefficient0.112656
Fraction of closed triangles0.0436041
Maximum k-core1.7K
Lower bound of Maximum Clique259

Network Data Preview

Interactive visualization of kron_g500-logn21'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 kron-g500-logn21 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.