scc-twitter-copen (Temporal Reachability Networks)
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This network dataset is in the category of Temporal Reachability Networks
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Metadata
| Category | Temporal Networks |
| Collection | Temporal Reachability Networks |
| Tags | |
| Source | http://www.ryanrossi.com/papers/maxclique_tscc.pdf |
| Short | Retweet temporal reachability graph |
| Vertex type | User |
| Edge type | Temporal path via retweets/mentions |
| Format | Undirected |
| Edge weights | Unweighted |
| Description | In 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:
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.
@inproceedings{nr,
title={The Network Data Repository with Interactive Graph Analytics and Visualization},
author={Ryan A. Rossi and Nesreen K. Ahmed},
booktitle={AAAI},
url={https://networkrepository.com},
year={2015}
}
@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}
}
@inproceedings{ahmed2010time,
title={Time-based sampling of social network activity graphs},
author={Ahmed, N.K. and Berchmans, F. and Neville, J. and Kompella, R.},
booktitle={SIGKDD MLG},
pages={1--9},
year={2010},
}
Network Data Statistics
| Nodes | 8.6K |
| Edges | 473.6K |
| Density | 0.0128686 |
| Maximum degree | 1.5K |
| Minimum degree | 0 |
| Average degree | 110 |
| Assortativity | -0.407823 |
| Number of triangles | 291.2M |
| Average number of triangles | 33.9K |
| Maximum number of triangles | 469K |
| Average clustering coefficient | 0.203078 |
| Fraction of closed triangles | 0.699313 |
| Maximum k-core | 583 |
| Lower bound of Maximum Clique | 541 |
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