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scc-fb-forum     (Temporal Reachability Networks)

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This network dataset is in the category of Temporal Reachability Networks



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Metadata

CategoryTemporal Networks
CollectionTemporal Reachability Networks
Tags
Sourcehttp://www.ryanrossi.com/papers/maxclique_tscc.pdf
ShortFacebook temporal reachability graph
Vertex typeUser
Edge typeTemporal path via forum posts
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.

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}
}

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.

@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}
}

@inproceedings{rossi2014pmc-www,
     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)},
     year={2014}
}

Network Data Statistics

Nodes897
Edges71K
Density0.176708
Maximum degree435
Minimum degree0
Average degree158
Assortativity-0.220219
Number of triangles21.9M
Average number of triangles24.4K
Maximum number of triangles70.4K
Average clustering coefficient0.466629
Fraction of closed triangles0.870529
Maximum k-core273
Lower bound of Maximum Clique262

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