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student_cooperation — Student cooperation (2012)

Description

Network of cooperation among 185 students of the mandatory "Computer and Network Security" course at Ben-Gurion University of the Negev, Israel, in 2012, from two different departments. Nodes are students and edges denote implicit or explicit cooperation between two students while doing their homework assignments, as inferred by M. Fire et al. from the data collected during the course. There are three types of links, and a pair of students can be connected by several edges of different types.

The network has the following properties:

  • name: the anonymous identifier of the student.
  • type: the type of link: Partners (explicit cooperation: the students submitted an assignment together), Computer (the students submitted assignments from the same computer), or Time (implicit cooperation inferred from the timing of the students' activity; see Fire et al. for the precise definitions).
Tags
Social Offline Multigraph Unweighted
Citation
  • M. Fire et al., "Predicting student exam's scores by analyzing social network data." International Conference on Active Media Technology, 584-595 (AMT 2012) 10.1007/978-3-642-35236-2_59 Sci-Hub
Upstream URL ☠
The upstream source has since vanished. The files can be temporarily accessed here.
Networks
Tip: hover over a column header for a description.
Column legend
$\left<k\right>$
Mean degree
$\sigma_k$
Standard deviation of the degrees
$\lambda_h$
Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$
Random walk mixing time
$r$
Degree assortativity
$c$
Global clustering coefficient
$\oslash$
(Pseudo-) diameter
$S$
Relative size of the largest component
NPs
Node properties
EPs
Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
student_cooperation 185 360 3.89 1.68 3.38 153.60 0.37 0.49 17 0.76 Undirected Unipartite name type 4 KiB 6 KiB 6 KiB 5 KiB
Ridiculograms*
* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.