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ia-reality-call     (Dynamic Networks)

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This network dataset is in the category of Dynamic Networks



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Metadata

CategorySparse networks, temporal networks
CollectionInteraction networks
Tags
Sourcehttp://realitycommons.media.mit.edu/realitymining.html
Shortuser-calls-user
Vertex typePerson
Edge typeCall
FormatUndirected
Edge weightsMultiple unweighted edges
MetadataTime (edges have timestamps)
DescriptionReality mining network data consists of human mobile phone call events between a small set of core users at the Massachusetts Institute of Technology (MIT) whom actually were assigned mobile phones for which all calls were collected. The data also contains calls from users outside this small set of users to other phones of individuals that were not actively monitored and thus these nodes generally have fewer edges than nodes within the small set of users at MIT that participated in the experiment and were assigned phones. The data was collected collected by the Reality Mining experiment performed in 2004 as part of the Reality Commons project. The data was collected over 9 months using 100 mobile phones. A node represents a person; an edge indicates a phone call or voicemail between two users. See http://realitycommons.media.mit.edu/realitymining.html for more details.

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.

@article{eagle2006reality,
     title={Reality mining: sensing complex social systems},
     author={Eagle, N. and Pentland, A.},
     journal={Personal and Ubiquitous Computing},
     volume={10},
     number={4},
     pages={255--268},
     year={2006},
}

Network Data Statistics

Nodes6.8K
Edges51.2K
Density0.00221103
Maximum degree3K
Minimum degree1
Average degree15
Assortativity-0.315687
Number of triangles923.7K
Average number of triangles135
Maximum number of triangles97.3K
Average clustering coefficient0.362203
Fraction of closed triangles0.0299949
Maximum k-core967
Lower bound of Maximum Clique116

Network Data Preview

Interactive visualization of ia-reality-call'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 ia-reality-call 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
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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.