Introduction to correlation networks: Interdisciplinary approaches beyond thresholding

TL;DR

This paper reviews diverse methods for constructing and analyzing correlation networks across disciplines, highlighting challenges of thresholding and proposing best practices and open questions for future research.

Contribution

It provides a comprehensive overview of correlation network methods beyond thresholding, fostering interdisciplinary understanding and proposing new approaches and open research questions.

Findings

Thresholding has limitations in correlation network construction.

Weighted and regularized correlation networks offer improved analysis.

The paper discusses threshold-free methods and null model comparisons.

Abstract

Many empirical networks originate from correlational data, arising in domains as diverse as psychology, neuroscience, genomics, microbiology, finance, and climate science. Specialized algorithms and theory have been developed in different application domains for working with such networks, as well as in statistics, network science, and computer science, often with limited communication between practitioners in different fields. This leaves significant room for cross-pollination across disciplines. A central challenge is that it is not always clear how to best transform correlation matrix data into networks for the application at hand, and probably the most widespread method, i.e., thresholding on the correlation value to create either unweighted or weighted networks, suffers from multiple problems. In this article, we review various methods of constructing and analyzing correlation networks, ranging from thresholding and its improvements to weighted networks, regularization, dynamic correlation networks, threshold-free approaches, comparison with null models, and more. Finally, we propose and discuss recommended practices and a variety of key open questions currently confronting this field.