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Updated: Jun 21, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Random hypergraphs and their applications.

Gourab Ghoshal¹, Vinko Zlatić, Guido Caldarelli

¹Department of Physics and Michigan Center for Theoretical Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|August 8, 2009

Summary

This summary is machine-generated.

This study models folksonomies, or user-resource-tag networks, as random hypergraphs. The model accurately predicts some network properties but differs due to multiple tagging practices.

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Last Updated: Jun 21, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Area of Science:

Social Network Analysis
Network Science
Information Science

Background:

Emergence of complex social networks in online communities.
Folksonomies represent a tripartite structure: users, resources, and tags.
Existing graph structures are insufficient for representing folksonomies.

Purpose of the Study:

To propose a mathematical model for tripartite folksonomy structures.
To represent folksonomies as random hypergraphs.
To analyze and compare model properties with real-world folksonomies.

Main Methods:

Development of a random hypergraph model for folksonomies.
Exact calculation of model properties in the limit of large network size.
Comparison of model predictions with empirical data from the Flickr folksonomy.

Main Results:

The random hypergraph model successfully captures some properties of real folksonomies.
Significant differences were observed between the model and empirical data.
Multiple tagging practices were identified as a key factor for discrepancies.

Conclusions:

Random hypergraphs provide a valuable framework for modeling folksonomies.
The model's limitations highlight the impact of user tagging behavior.
Further research can refine models to account for complex tagging patterns.