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hollywood-2009     (Collaboration Networks)

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This network dataset is in the category of Collaboration Networks



Visualize ca-hollywood-2009's link structure and discover valuable insights using the interactive network data visualization and analytics platform. Compare with hundreds of other network data sets across many different categories and domains.

Metadata

CategorySparse Networks
CollectionCollaboration networks
Tags
ShortGraph of movie actors
Vertex typeActors
Edge typeAppeared-in-movie relationships
DescriptionOne of the most popular undirected collaboration networks of movie actors. Nodes are actors, and two actors form an edge between them whenever they appeared in a movie together.

Please cite the following if you use the data:

@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}
}

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{BoVWFI,
     author ={Paolo Boldi and Sebastiano Vigna},
     title = {The {W}eb{G}raph Framework {I}: {C}ompression Techniques},
     year = {2004},
     booktitle= {Proc. of the Thirteenth International World Wide Web Conference (WWW 2004)},
     address={Manhattan, USA},
     pages={595--601},
     publisher={ACM Press}
}

Network Data Statistics

Nodes1.1M
Edges56.3M
Density9.85218e-05
Maximum degree11.5K
Minimum degree1
Average degree105
Assortativity0.350902
Number of triangles14.7B
Average number of triangles13.8K
Maximum number of triangles4M
Average clustering coefficient0.766355
Fraction of closed triangles0.309554
Maximum k-core2.2K
Lower bound of Maximum Clique53

Network Data Preview

Interactive visualization of ca-hollywood-2009's graph structure

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Interactive Visualization of Node-level Properties and Statistics

Tools for Interactive Exploration of Node-level Statistics

Visualize and interactively explore ca-hollywood-2009 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.