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web-google     (Web Graphs)

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This network is in the collection of Web Graphs





Visualize web-google'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

ShortWeb graph from Google
Vertex typeWeb page
Edge typeHyperlink
DescriptionWeb graph where nodes represent web pages and directed edges represent hyperlinks between them. The data was released in 2002 by Google as a part of Google Programming Contest.

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.

@article{leskovec2009community,
     title={Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters},
     author={Leskovec, Jure and Lang, Kevin J and Dasgupta, Anirban and Mahoney, Michael W},
     journal={Internet Mathematics},
     volume={6},
     number={1},
     pages={29--123},
     year={2009},
     publisher={Taylor \& Francis}
}

Network Statistics

Nodes1.3K
Edges2.8K
Density0.00328924
Maximum degree59
Minimum degree1
Average degree4
Assortativity-0.0546939
Number of triangles15.2K
Average number of triangles11
Maximum number of triangles189
Average clustering coefficient0.348321
Fraction of closed triangles0.531962
Maximum k-core18
Lower bound of Maximum Clique18

Data Preview

Interactive visualization of web-google'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

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Interactive Visualization of Node-level Properties and Statistics

Tools for Interactive Exploration of Node-level Statistics

Visualize and interactively explore web-google 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.
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Interactive Visualization of Node-level Feature Distributions

Node-level Feature Distributions

degree distribution

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degree CDF

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degree CCDF

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kcore distribution

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kcore CDF

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kcore CCDF

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triangle distribution

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triangle CDF

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triangle CCDF

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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.