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Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
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Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

Hypergraph topological quantities for tagged social networks.

Vinko Zlatić1, Gourab Ghoshal, Guido Caldarelli

  • 1CNR-INFM Centro SMC Dipartimento di Fisica, Università di Roma Sapienza, Piazzale A Moro 5, 00185 Roma, Italy.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 13, 2009
PubMed
Summary
This summary is machine-generated.

This study models folksonomies, or user-generated tagging systems, using tripartite hypergraphs. The research provides new tools to analyze the structure of these complex social networks.

Related Experiment Videos

Last Updated: Jun 18, 2026

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

Area of Science:

  • Network Science
  • Information Science
  • Computer Science

Background:

  • Social networks are evolving, necessitating new methods to represent complex graph structures.
  • Folksonomies, like user tagging systems, organize undifferentiated data through collaborative tagging.
  • Previous work introduced a mathematical model for folksonomies as tripartite hypergraphs.

Purpose of the Study:

  • To extend the tripartite hypergraph model for folksonomies.
  • To define and measure additional topological and structural quantities.
  • To establish a standard methodology for analyzing tagged networks.

Main Methods:

  • Extending a mathematical model of tripartite hypergraphs.
  • Defining and calculating quantities: edge distributions, vertex similarity, correlations, and clustering.
  • Empirical measurement on real-world folksonomies (Flickr, CiteULike).

Main Results:

  • The extended model captures key structural features of folksonomies.
  • Analysis reveals shared qualitative features with other complex networks.
  • Folksonomies exhibit specific patterns in edge distributions, vertex similarity, and clustering.

Conclusions:

  • The proposed quantities and methodology offer a standardized approach to analyzing tagged networks.
  • The findings contribute to understanding the structure of large-scale collaborative tagging systems.
  • This framework can be applied to various folksonomy platforms for network analysis.