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swingers — Swingers and parties (2013)

Description

Bipartite (two-mode) sexual affiliation network of swinger couples and the swinger parties they attended, from the study of A.-M. Niekamp et al. on the spread of sexually transmitted infections among swingers (heterosexuals who engage in sexual activity with partners outside their relationship). Each couple ("swing unit") is represented by a single node, and an edge connects a couple to a party it attended. There are 57 couples and 39 parties. The data were distributed as a two-mode matrix by the UCINET project. Edges are directed from couples to parties, although the direction carries no additional information.

The network has the following properties:

  • (Node) name: the label of the node in the upstream matrix (numbers for couples, letters for parties).
  • (Node) is_party: whether the node is a party (true) or a couple (false).
Tags
Social Offline Unweighted
Added
2020-08-11
Citation
  • A.-M. Niekamp et al., "A sexual affiliation network of swingers, heterosexuals practicing risk behaviours that potentiate the spread of sexually transmitted infections: A two-mode approach." Social Networks 35(2), 223-236 (2013) 10.1016/j.socnet.2013.02.006 Sci-Hub
Upstream URL OK
https://sites.google.com/site/ucinetsoftware/datasets/covert-networks/swingers
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
swingers 96 232 2.42 5.19 7.46 5.19 -0.34 0.00 7 1.00 Directed Bipartite name 2 KiB 3 KiB 3 KiB 2 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.