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Decoding Natural Behavior from Neuroethological Embedding

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Building surrogate temporal network data from observed backbones.

Charley Presigny¹, Petter Holme², Alain Barrat^1,2

¹Aix Marseille Univ, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, 13288 Marseille, France.

Physical Review. E

|June 17, 2021

Summary

This summary is machine-generated.

Researchers developed methods to create actionable surrogate data from temporal network backbones. This process helps retain essential information for accurate simulations of spreading processes on complex networks.

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Area of Science:

Complex Systems Science
Network Science
Data Science

Background:

Temporal networks represent socioeconomic systems but contain noise and non-essential data.
Extracting a network backbone simplifies data but raises concerns about its usability in simulations.
Reduced network views may introduce biases if not properly decompressed for data-driven analysis.

Purpose of the Study:

To develop systematic procedures for building actionable surrogate data from temporal network backbones.
To determine the necessary information to retain alongside a backbone for accurate data-driven simulations.
To explore the creation of ensembles of surrogate data from a single dataset without modeling assumptions.

Main Methods:

Proposed and explored systematic procedures for generating surrogate data from temporal network backbones.
Investigated information retention requirements for backbone-derived surrogate data.
Utilized empirical temporal networks with diverse structures for validation.

Main Results:

Demonstrated that surrogate data can be effectively generated from temporal network backbones.
Identified the amount of information needed to preserve backbone data for accurate simulation of spreading processes.
Showcased the ability to create ensembles of surrogate data from a single dataset.

Conclusions:

Developed actionable methods for summarizing complex temporal network data.
Provided insights into retaining sufficient information within network backbones for reliable data-driven simulations.
Enabled the generation of diverse surrogate datasets from empirical temporal networks without explicit modeling.