The Loyvain Link: Unifying Brain Networks and Machine Learning

In this episode of VALIANT Pulse, we explore a bold attempt to unify aspects unsupervised learning, network science, and imaging neuroscience through the lens of brain data. Mika Rubinov from Vanderbilt describes a series of analytical equivalences and unifications—connecting modularity and k-means, canonical covariance and co-clustering, diffusion maps and co-neighbor matrices—all with mathematical rigor and a nod to neuroscience's central patterns. These insights challenge how we analyze, interpret, and even discover structure in the brain, providing both a toolbox and a philosophical shift. Join us as we trace the common threads—and uncover surprising overlaps—across disciplines once thought distinct. The views expressed reflect an individual interpretation of the work, not official peer-reviewed consensus.

For more details: https://zenodo.org/records/15340856