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Related Experiment Video

Updated: May 10, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Weighted projected networks: mapping hypergraphs to networks.

Eduardo López¹

¹CABDyN Complexity Centre, Saïd Business School, University of Oxford, Park End Street, Oxford OX1 1HP, United Kingdom. eduardo.lopez@sbs.ox.ac.uk

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

|June 18, 2013

Summary

This study introduces a new model where complex multiway interactions in systems can be understood through weighted pairwise networks. This approach reveals hidden multiway structures in network data and enhances network analysis.

Related Experiment Videos

Last Updated: May 10, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Network Science
Complex Systems Analysis
Graph Theory

Background:

Many real-world systems feature multiway interactions but are analyzed using pairwise interactions.
Existing methods often overlook the rich information present in higher-order interactions.

Purpose of the Study:

To propose a novel modeling framework where pairwise interactions emerge from multiway interactions.
To analytically characterize the statistical properties of these derived weighted projected networks.
To identify signatures of multiway interactions within weighted network data.

Main Methods:

Modeling systems as ensembles of hypergraphs.
Applying projections to generate ensembles of weighted projected networks.
Analytical calculation of statistical properties, including percolation thresholds.

Main Results:

Weighted projected networks exhibit inherent weight disorder.
A distinct signature of multiway interactions appears in link weights as hypergraph size changes.
The transition in projected networks is identified as second order.

Conclusions:

The proposed framework offers a new perspective on understanding weighted networks.
The identified signature can aid in detecting hidden multiway interactions in empirical data.
This approach bridges the gap between multiway and pairwise network representations.