ArticleStatistical analysis and data mining2021
A framework for stability-based module detection in correlation graphs.
Article in Statistical analysis and data mining, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- From clusters to clinical rules: unsupervised machine learning identifies four newborn hearing phenotypes with bedside risk stratification.BMC pediatrics · 2026Article
- Comprehensive Analysis Reveals Potential Molecular Targets in Juvenile Dermatomyositis.Biochemistry research international · 2026Article
- Systematic review and meta-analysis of disease clustering in multimorbidity: a study protocol.BMJ open · 2023Article
- A framework for stability-based module detection in correlation graphs.Statistical analysis and data mining · 2021Article
- Stability estimation for unsupervised clustering: A review.Wiley interdisciplinary reviews. Computational statisticsReview
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Authors and funding
6 authors.
Funding
Abstract
Graphs can be used to represent the direct and indirect relationships between variables, and elucidate complex relationships and interdependencies. Detecting structure within a graph is a challenging problem. This problem is studied over a range of fields and is sometimes termed community detection, module detection, or graph partitioning. A popular class of algorithms for module detection relies on optimizing a function of modularity to identify the structure. In practice, graphs are often learned from the data, and thus prone to uncertainty. In these settings, the uncertainty of the network structure can become exaggerated by giving unreliable estimates of the module structure. In this work, we begin to address this challenge through the use of a nonparametric bootstrap approach to assessing the
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Registered trials
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.