Login to your profile!



No account? sign up!

web-hudong     (Web Graphs)

Download network data

This network is in the collection of Web Graphs





Visualize web-hudong'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

Tags
Vertex typePage
Edge typeHyperlink
FormatDirected
Edge weightsUnweighted
DescriptionA directed network of hyperlinks between the articles of the Chinese online encyclopedia Hudong.

Citing the repository in published materials

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

@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}
}

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{hudong-xing,
     author = {Xing Niu and Xinruo Sun and Haofen Wang and Shu Rong and Guilin Qi and Yong Yu},
     title = {{Zhishi.me} -- Weaving {Chinese} Linking Open Data},
     booktitle = {Proc. Int. Semantic Web Conf.},
     year = {2011},
     pages = {205--220},
}

Network Statistics

Nodes2M
Edges14.7M
Density7.53079e-06
Maximum degree61.6K
Minimum degree1
Average degree14
Assortativity-0.220483
Number of triangles139.1M
Average number of triangles70
Maximum number of triangles327.5K
Average clustering coefficient0.0892735
Fraction of closed triangles0.00736266
Maximum k-core529
Lower bound of Maximum Clique267

Data Preview

Interactive visualization of web-hudong'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

Loading...

Interactive Visualization of Node-level Properties and Statistics

Tools for Interactive Exploration of Node-level Statistics

Visualize and interactively explore web-hudong 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.
Note: You are not logged in!
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

degree distribution

Loading...

degree CDF

Loading...

degree CCDF

Loading...

kcore distribution

Loading...

kcore CDF

Loading...

kcore CCDF

Loading...

triangle distribution

Loading...

triangle CDF

Loading...

triangle CCDF

Loading...

All visualizations and analytics are interactive and flexible for exploratory analysis and data mining in real-time and include the following features:

  • Degree, k-core, triangles, and triangle-core distributions. We include plots for each of the fundamental graph features and counts of the number with a particular property (i.e., number of nodes that form k triangles or have degree k, etc.)
  • We also include the CDF and CCDF distributions for each graph in the collection.
  • All visualizations and plots are zoomable. One may zoom-in or out on the data visualization using scrolling.
  • Panning. Users may also click anywhere on the plot and move the mouse in any direction to pan.
  • Adjust scale and other application dependent-parameters. All interactive visualizations may adjust the scale which is particularly important in certain types of graph data that contain highly skewed graph properties (power-lawed graphs and/or networks) such as degree distribution.

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.