What is the spectral interpretation of Random Forest similarity?

Random Forest proximity matrices can be interpreted using spectral graph theory.

The proximity matrix defines a graph where nodes are samples and edge weights represent co-occurrence in leaves. Spectral decomposition of this graph reveals cluster structures and latent manifolds. This connects RF to manifold learning and graph-based semi-supervised learning methods.