scc-rt-libya (Temporal Reachability Networks)
Download network data
This network is in the collection of Temporal Reachability Networks
Visualize scc-rt-libya's link structure and discover valuable insights using our interactive graph visualization platform. Compare with hundreds of other networks across many different collections and types.
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 | 5K |
| Edges | 26 |
| Density | 2.06305e-06 |
| Maximum degree | 11 |
| Minimum degree | 0 |
| Average degree | 0 |
| Assortativity | -0.18845 |
| Number of triangles | 15 |
| Average number of triangles | 0 |
| Maximum number of triangles | 5 |
| Average clustering coefficient | 0.0013857 |
| Fraction of closed triangles | 0.194805 |
| Maximum k-core | 3 |
| Lower bound of Maximum Clique | 3 |
Data Preview
Interactive visualization of scc_rt_libya's graph structure
Interactively explore the networks graph structure!
- Use mouse wheel to zoom in/out
- Mouseover nodes to see their degree
- Drag network to see more details
Interactive Visualization of Node-level Properties and Statistics
Tools for Interactive Exploration of Node-level Statistics
Visualize and interactively explore scc-rt-libya and its important node-level statistics!
- Each point represents a node (vertex) in the graph.
- A subset of interesting nodes may be selected and their properties may be visualized across all node-level statistics. To select a subset of nodes, hold down the left mouse button while dragging the mouse in any direction until the nodes of interest are highlighted.This feature allows users to explore and analyze various subsets of nodes and their important interesting statistics and properties to gain insights into the graph data
- Zoom in/out on the visualization you created at any point by using the buttons below on the left.
- Once a subset of interesting nodes are selected, the user may further analyze by selecting and drilling down on any of the interesting properties using the left menu below.
- We also have tools for interactively visualizing, comparing, and exploring the graph-level properties and statistics.
Please login or join the community to leverage the many other tools and features available in our interactive graph analytics platform.
Interactive Visualization of Node-level Feature Distributions
Node-level Feature Distributions
Discuss and Share
Collaborate and contribute to the first interactive and community-oriented data repository!
Share key insights, awesome visualizations, or simply discuss advantages of data, any observed or known properties, challenges, problems, corrections, and any other helpful comments! Post and discuss recent published works that utilize this dataset (including your own). Any and all feedback is welcome and encouraged.