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ia-escorts-dynamic     (Dynamic Networks)

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This network dataset is in the category of Dynamic Networks



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Metadata

ShortSexual escorts
Vertex typeBuyer, escort
Edge typeRating
FormatBipartite
Edge weightsMultigraph, weighted
DescriptionThis network is a bipartite graph of sex buyers and their escorts. Nodes are buyers and escorts and an edge denotes sexual intercourse between a male sex-buyer and a female escort. Edges are weighted with the rating of the escort given by the buyer. The possible ratings are: bad (−1), neutral (0), good (+1). Each row represents an edge and the data in each row is space-delimited. The third column represents the rating for that edge and the fourth column represents the timestamp of the edge.

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={http://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{escortsRocha2010,
     author = {Rocha, Luis E. C. and Liljeros, Fredrik and Holme, Petter},
     journal = {Proc. of the National Academy of Sciences},
     number = 13,      pages = {5706--5711},
     title = {Information Dynamics Shape the Sexual Networks of {Internet}-mediated Prostitution},
     volume = 107,      year = 2010}

Network Data Statistics

Nodes10.1K
Edges50.6K
Density0.000991352
Maximum degree616
Minimum degree1
Average degree10
Assortativity-0.0625723
Number of triangles23.4K
Average number of triangles2
Maximum number of triangles2.8K
Average clustering coefficient0.0117455
Fraction of closed triangles0.00790434
Maximum k-core42
Lower bound of Maximum Clique4

Network Data Preview

Interactive visualization of ia-escorts-dynamic'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-escorts-dynamic 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.

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