Introduction to correlation networks: Interdisciplinary approaches beyond thresholding

Physics Reports, vol. 1136, pp. 1–39

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.

Authors 4

1. Naoki Masuda

   University at Buffalo, State University of New York

   Affiliation as printed

   Department of Mathematics, State University of New York at Buffalo, USA

   Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, USA

2. Zachary M. Boyd

   Brigham Young University

   Affiliation as printed

   Department of Mathematics, Brigham Young University, USA

3. Diego Garlaschelli Aachen Lorentz Institute for Theoretical Physics

   Leiden University · IMT School for Advanced Studies Lucca

   Affiliation as printed

   IMT School of Advanced Studies, Lucca, Italy

   Lorentz Institute for Theoretical Physics, Leiden University, The Netherlands

4. Peter J. Mucha

   Dartmouth College

   Affiliation as printed

   Department of Mathematics, Dartmouth College, USA