# Constructing networks by filtering correlation matrices: A null model approach

## TL;DR

This paper introduces a null model-based regularization method for constructing networks from correlation matrices, effectively filtering out spurious correlations and improving predictive accuracy over traditional thresholding methods.

## Contribution

It proposes a flexible algorithm that integrates null models with regularization for network construction, allowing model selection among candidates and demonstrating improved results on economic data.

## Key findings

- Configuration null model often preferred for economic data
- Method outperforms standard correlation thresholding in predicting exports
- Flexible null model integration enhances network filtering

## Abstract

Network analysis has been applied to various correlation matrix data. Thresholding on the value of the pairwise correlation is probably the most straightforward and common method to create a network from a correlation matrix. However, there have been criticisms on this thresholding approach such as an inability to filter out spurious correlations, which have led to proposals of alternative methods to overcome some of the problems. We propose a method to create networks from correlation matrices based on optimisation with regularization, where we lay an edge between each pair of nodes if and only if the edge is unexpected from a null model. The proposed algorithm is advantageous in that it can be combined with different types of null models. Moreover, the algorithm can select the most plausible null model from a set of candidate null models using a model selection criterion. For three economic data sets, we find that the configuration model for correlation matrices is often preferred to standard null models. For country-level product export data, the present method better predicts main products exported from countries than sample correlation matrices do.