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student-coop     (Social Networks)

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This network dataset is in the category of Social Networks



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Metadata

ShortStudents' Cooperation social network
DescriptionStudents’ Cooperation Social Network was constructed from the data collected during a “Computer and Network Security” course; a mandatory course at Ben-Gurion University. The social network contains data collected from 185 participating students from two different departments. The course’s social network was created by analyzing the implicit and explicit cooperation among the students while doing their homework assignments. The students’ cooperation graph contained 185 nodes and 360 links and 3 types of links.

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{Fire2012,
     title={Predicting Student Exam's Scores by Analyzing Social Network Data},
     author={Michael Fire, Gilad Katz, Yuval Elovici, Bracha Shapira, and Lior Rokach},
     booktitle={Active Media Technology},
     pages={584--595},
     year={2012},
     publisher={Springer} }

Network Data Statistics

Nodes7.5M
Edges1
Density3.55833e-14
Maximum degree1
Minimum degree0
Average degree0
Assortativitynan
Number of triangles0
Average number of triangles0
Maximum number of triangles0
Average clustering coefficient0
Fraction of closed trianglesnan
Maximum k-core2
Lower bound of Maximum Clique2

Network Data Preview

Interactive visualization of soc-student-coop's graph structure

Interactively explore the networks graph structure!

  • Use mouse wheel to zoom in/out
  • Mouseover nodes to see their degree
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Interactive Visualization of Node-level Properties and Statistics

Tools for Interactive Exploration of Node-level Statistics

Visualize and interactively explore soc-student-coop 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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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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