digg (Social Networks)
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This network dataset is in the category of Social Networks
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Metadata
| Short | Digg online social network |
| Description | Digg is a social news website. The dataset contains all links among users. |
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={http://networkrepository.com},
year={2015}
}
@article{zafarani2014users,
title={Users Joining Multiple Sites: Distributions and Patterns},
author={Zafarani, Reza and Liu, Huan},
booktitle = {Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (ICWSM'14)},
year={2014}
}
Network Data Statistics
| Nodes | 770.8K |
| Edges | 5.9M |
| Density | 1.9885e-05 |
| Maximum degree | 17.6K |
| Minimum degree | 1 |
| Average degree | 15 |
| Assortativity | -0.092109 |
| Number of triangles | 188.1M |
| Average number of triangles | 244 |
| Maximum number of triangles | 396.6K |
| Average clustering coefficient | 0.0880935 |
| Fraction of closed triangles | 0.0466703 |
| Maximum k-core | 237 |
| Lower bound of Maximum Clique | 45 |
Network Data Preview
Interactive visualization of soc-digg's graph structure
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Interactive Visualization of Node-level Properties and Statistics
Tools for Interactive Exploration of Node-level Statistics
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- 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
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Interactive Visualization of Node-level Feature Distributions
Node-level Feature Distributions
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