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scc-retweet-crawl     (Temporal Reachability Networks)

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This network is in the collection of Temporal Reachability Networks

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CategoryTemporal Networks
CollectionTemporal Reachability Networks
ShortRetweet temporal reachability graph
Vertex typeUser
Edge typeTemporal path via retweets
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:

     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},

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.

     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},

     title={Fast Maximum Clique Algorithms for Large Graphs},
     author={Ryan A. Rossi and David F. Gleich and Assefaw H. Gebremedhin and Mostofa A. Patwary},
     booktitle={Proceedings of the 23rd International Conference on World Wide Web (WWW)},

Network Statistics

Maximum degree195
Minimum degree0
Average degree0
Number of triangles73.2K
Average number of triangles0
Maximum number of triangles1.1K
Average clustering coefficient0.00405199
Fraction of closed triangles0.293542
Maximum k-core20
Lower bound of Maximum Clique20

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