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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Reconstructing networks from simple and complex contagions.

Nicholas W Landry^1,2,3, William Thompson¹, Laurent Hébert-Dufresne^1,2

¹Vermont Complex Systems Center, <a href="https://ror.org/0155zta11">University of Vermont</a>, Burlington, Vermont 05405, USA.

Physical Review. E

|November 20, 2024

Summary

This summary is machine-generated.

We developed a new method to reconstruct network contagions, moving beyond simple models. This approach better captures complex dynamics, improving network analysis and understanding of information spread.

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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

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Published on: September 25, 2021

Area of Science:

Network Science
Computational Social Science
Data Analysis

Background:

Network contagion models often simplify dynamics, limiting their applicability.
Existing tools struggle to fit complex contagion processes.
A gap exists in methods for reconstructing networks with complex dynamics.

Purpose of the Study:

To develop a novel method for reconstructing networks and their dynamics from time-series node states.
To bridge the gap between simple and complex contagion models.
To identify conditions favoring the reconstruction of networks under different contagion models.

Main Methods:

Developed a nonparametric method for network and dynamics reconstruction.
Introduced a model that integrates simple pairwise and complex neighborhood contagions.
Analyzed network reconstructability based on network density and dynamic saturation.

Main Results:

The new method successfully reconstructs networks and dynamics from node state data.
Network reconstruction is more accurate for dense networks or saturating dynamics under complex contagions.
Simple contagion models are more suitable for sparse networks or non-saturating dynamics.

Conclusions:

The developed method offers a flexible approach to modeling network contagions.
Understanding network structure and dynamics is crucial for accurate contagion modeling.
The findings guide the choice of contagion models based on network properties and dynamics.