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Temporal Networks

Temporal Networks

Download temporal network datasets.
Dynamic network data (temporal network data) typically consists of a sequence of edges with timestamps. Such temporal networks are sometimes called edge or graph streams.

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

Citation and acknowledgement policy

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://networkrepository.com},
      year={2015}
  }



Acknowledgement & Citation Policy

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://networkrepository.com},
     year={2015}
}


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.