scc-rt-saudi     (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 |
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}
}
Network Statistics
Nodes | 6.8K |
Edges | 91 |
Density | 3.93078e-06 |
Maximum degree | 16 |
Minimum degree | 0 |
Average degree | 0 |
Assortativity | 0.118004 |
Number of triangles | 546 |
Average number of triangles | 0 |
Maximum number of triangles | 62 |
Average clustering coefficient | 0.00259969 |
Fraction of closed triangles | 0.6825 |
Maximum k-core | 8 |
Lower bound of Maximum Clique | 8 |
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