### scc-twitter-copen (Temporal Reachability Networks)

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This network is in the collection 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. |

#### Citing the repository in published materials

If you find Network Repository useful for your research, please consider citing the following paper:

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.

`@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}

}

`@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 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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