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scc-rt-mittromney     (Temporal Reachability Networks)

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This network is in the collection of Temporal Reachability Networks





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Metadata

CategoryTemporal Networks
CollectionTemporal Reachability Networks
Tags
Sourcehttp://www.ryanrossi.com/papers/maxclique_tscc.pdf
ShortRetweet temporal reachability graph
Vertex typeUser
Edge typeTemporal path via retweets
FormatUndirected
Edge weightsUnweighted
DescriptionIn networks where edges represent a contact, a phone-call, an email, or physical proximity between two entities at a specific point in time, one gets an evolving network structure. One useful way to investigate temporal networks is to transform the temporal graph (sequence of timestamped edges) into a (static) temporal reachability graph representing the possible flow of information/influence, etc. The temporal reachability graph is formed by placing an edge in the temporal reachability graph if there exists a "strong" temporal path between two vertices (in both directions: from u to v, and from v to u). Hence, a temporal path represents a sequence of contacts that obeys time and therefore an edge in the temporal reachability graph represents the fact that a user could have transmitted a piece of information (or disease, etc) to that user (and vice-versa). This temporal graph representation is extremely useful for analyzing such networks and for planning applications. For instance, a temporal strong component is a set of vertices where all pairwise temporal paths exist.

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}
}

Note that if you transform/preprocess this data 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.

@article{rossi2012fastclique,
     title={What if CLIQUE were fast? Maximum Cliques in Information Networks and Strong Components in Temporal Networks},
     author={Ryan A. Rossi and David F. Gleich and Assefaw H. Gebremedhin and Mostofa A. Patwary},
     journal={arXiv preprint arXiv:1210.5802},
     pages={1--11},
     year={2012}
}

Network Statistics

Nodes7.9K
Edges108
Density3.50566e-06
Maximum degree25
Minimum degree0
Average degree0
Assortativity-0.083512
Number of triangles198
Average number of triangles0
Maximum number of triangles38
Average clustering coefficient0.00266292
Fraction of closed triangles0.294643
Maximum k-core5
Lower bound of Maximum Clique5

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