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Updated: Feb 15, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

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Published on: August 5, 2014

Sampling of temporal networks: Methods and biases.

Luis E C Rocha¹, Naoki Masuda², Petter Holme³

¹Department of Public Health Sciences, Karolinska Institutet, 17177 Stockholm, Sweden and Department of Mathematics, Université de Namur, 5000 Namur, Belgium.

Physical Review. E

|January 20, 2018

Summary

Sampling temporal networks can alter their structure. Uniform node sampling generally performs best across various network statistics and real-world systems, minimizing biases in data analysis.

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Area of Science:

Network Science
Computational Social Science
Epidemiology

Background:

Temporal networks model dynamic systems like epidemic spread.
Real-world networks are often sampled, affecting their structure.
Subsampling is used for visualization and computation, but can introduce bias.

Purpose of the Study:

To evaluate biases introduced by different temporal network sampling strategies.
To identify robust sampling methods for temporal network analysis.
To guide data collection and interpretation of sampled temporal network data.

Main Methods:

Applied four distinct sampling strategies to diverse real-life temporal networks.
Quantified sampling-induced biases on network statistics (link activity, temporal paths, epidemic spread).
Compared the performance of sampling methods across various network types and metrics.

Main Results:

Identified common biases across different sampling strategies and network statistics.
Uniform node sampling demonstrated superior performance in most evaluated scenarios.
The impact of sampling varied depending on network characteristics and research questions.

Conclusions:

Sampling methods significantly influence temporal network structure and analysis outcomes.
Uniform node sampling is a recommended strategy for minimizing bias.
Problem-oriented selection of sampling methods is crucial for accurate temporal network research.