Unifying equivalences across unsupervised learning, network science, and imaging/network neuroscience.

ABSTRACT: Modern scientific fields face the challenge of integrating a wealth of data, analyses, and results. We recently showed that a neglect of this integration can lead to circular analyses and redundant explanations. Here, we help advance scientific integration by describing equivalences that unify diverse analyses of datasets and networks. We describe equivalences across analyses of clustering and dimensionality reduction, network centrality and dynamics, and popular models in imaging and network neuroscience. First, we equate foundational objectives across unsupervised learning and network science (from k -means to modularity to UMAP), fuse classic algorithms for optimizing these objectives, and extend these objectives to simplify interpretations of popular dimensionality reduction methods. S

SUBMITTER: Rubinov M 

PROVIDER: S-EPMC12364054 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature