🤖 AI Summary
This study addresses pervasive issues—including information loss and poor reproducibility—arising from threshold-based correlation network construction in psychology, neuroscience, and genomics. We propose a threshold-free integrative modeling framework that unifies regularized estimation, dynamic correlation network modeling, threshold-free sparse learning, and null-model-driven statistical inference. The method synergistically integrates network science, time-series analysis, multiple hypothesis testing, and interpretable sparse modeling, prioritizing cross-disciplinary applicability and result robustness. To our knowledge, this is the first work to establish a comprehensive methodology landscape for correlation network inference across domains. It delivers a standardized analytical guideline, reusable evaluation benchmarks, and evidence-based practical recommendations—substantially enhancing comparability, reproducibility, and domain adaptability of network inference.
📝 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.
