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sociopatterns-hypertext     (Miscellaneous Networks)

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This network dataset is in the category of Miscellaneous Networks



Visualize sociopatterns-hypertext'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

NameHypertext 2009
CategorySparse Networks
CollectionHY
ShortHypertext 2009
Vertex typeVisitor
Edge typeContact
FormatUndirected
Edge weightsMultiple unweighted edges, Edges have timestamps
DescriptionThis is the network of face-to-face contacts of the attendees of the ACM Hypertext 2009 conference. The ACM Conference on Hypertext and Hypermedia 2009 (HT 2009, http://www.ht2009.org/) was held in Turin, Italy over three days from June 29 to July 1, 2009. In the network, a node represents a conference visitor, and an edge represents a face-to-face contact that was active for at least 20 seconds. Multiple edges denote multiple contacts. Each edge is annotated with the time at which the contact took place.

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.

Network Data Statistics

Nodes113
Edges4.4K
Density0.6941
Maximum degree98
Minimum degree0
Average degree38.87
Assortativity-0.01
Number of triangles49.7K
Average number of triangles439.69
Maximum number of triangles1.7K
Average clustering coefficient0.525
Fraction of closed triangles0.491
Maximum k-core28
Maximum k-truss16
Upper bound of Chromatic number22

Network Data Preview

Interactive visualization of sociopatterns-hypertext's graph structure

Interactively explore the networks graph structure!

  • Use mouse wheel to zoom in/out
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Interactive Visualization of Node-level Properties and Statistics

Tools for Interactive Exploration of Node-level Statistics

Visualize and interactively explore sociopatterns-hypertext 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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coloring distribution

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coloring CDF

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coloring CCDF

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kcore distribution

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kcore CDF

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kcore CCDF

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tcore distribution

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tcore CDF

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tcore 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.