A general Monte Carlo method for sample size analysis in the context of network models

Area of Science:

• Statistics
• Network Analysis
• Computational Methods

Background:

• Accurate sample size determination is crucial for reliable network model analysis.
• Existing methods for sample size computation in network analysis are often limited or complex.
• Cross-sectional network models are widely used across various scientific disciplines.

Purpose of the Study:

• To introduce a general, automated method for sample size computation in cross-sectional network models.
• To develop a flexible algorithm adaptable to different network structures and performance criteria.
• To provide researchers with a practical tool for determining adequate sample sizes.

Main Methods:

• An automated Monte Carlo algorithm is proposed, iteratively focusing on relevant sample sizes.
• The method requires inputs on network structure, performance measure targets, and statistic-based criteria.
• It involves Monte Carlo simulation, curve-fitting for interpolation, and stratified bootstrapping for uncertainty quantification.

Main Results:

• The method demonstrated good performance, yielding sample size recommendations close to benchmark values (average difference of 3 observations).
• Evaluated for Gaussian Graphical Models, the approach showed high accuracy with a standard deviation of 25.87 observations.
• The algorithm effectively balances computational efficiency with precise sample size determination.

Conclusions:

• The developed method offers a robust and efficient solution for sample size calculations in cross-sectional network analysis.
• The associated R package, powerly, is readily available, facilitating its application in research.
• This tool enhances the reliability and validity of findings derived from network models.