Evidence map›Paper›PMID 40814354›Full record

ArticlePhysics reports2025

Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.

Naoki Masuda, Zachary M Boyd, Diego Garlaschelli, Peter J Mucha

Abstract read
In one paragraph

Article in Physics reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Naoki MasudaDepartment of Mathematics, State University of New York at Buffalo, USA.
Zachary M BoydDepartment of Mathematics, Brigham Young University, USA.
Diego GarlaschelliLorentz Institute for Theoretical Physics, Leiden University, The Netherlands.
Peter J MuchaDepartment of Mathematics, Dartmouth College, USA.

Funding

Metabolomic Signatures of Urologic Chronic Pelvic Pain SyndromeR01DK125860 · NIDDK · WASHINGTON UNIVERSITY · PI HENDERSON, JEFFREY P · 2021 to 2025
$2.8M
DMS/NIGMS 1: Multilayer network approach to tandem repeat variation in genomesR01GM148973 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI MASUDA, NAOKI · 2022 to 2024
$444k
NIDDK NIH HHS R01 DK125860NIGMS NIH HHS R01 GM148973
6 · The paper itself

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.

Identifiers

PMID40814354
PMCPMC12345407

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.