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