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Multi-level Graph Visualization: From Global to Local Graph Properties

Select a network below for a multi-level graph visualization that leverages both local and global graph properties, as well as additional features and tools including:
  • interactive network visualizations,
  • global network statistics,
  • local node-level network stats & features, and
  • interactive visualizations of the important network distributions.

Note: Networks may also be sorted by the statistics.

Global Network Statistics

Summary of notation.
|V|
Number of nodes
|E|
Number of edges
dmax
Maximum degree
davg
Average degree
r
Assort. Coeff.
|T|
Number of triangles (3-clique)
|T|avg
Average triangles formed by a edge
|T|max
Maximum number of triangles formed by a edge
κavg
Average local clustering coefficient
κ
Global clustering coefficient
Kmax
Maximum k-core number
ωlb
Lower bound on the size of the maximum clique



Citing the repository

Citing the repository in published materials

If you find Network Repository useful for your research, please consider citing the following paper:

@inproceedings{nr,
     title={The Network Data Repository with Interactive Graph Analytics and Visualization},
     author={Ryan A. Rossi and Nesreen K. Ahmed},
     booktitle={Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence},
     url={http://networkrepository.com},
     year={2015}
}

A few of the datasets have additional citation requests; these can be found at the bottom of each dataset page.
Note that if you transform/preprocess any data obtained from NetworkRepository for your own research, we ask that you please share the data by uploading it along with details on the transformation and reference to any published materials.


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.