We compare two canonical ways to recover hierarchical clustering from an underlying density $f$: the \emph{density cluster tree} given by connected components of superlevel sets $\{f\geλ\}$, and a \emph{spectral hierarchy} obtained by applying single linkage to diffusion distances induced by a Laplacian operator. We give conditions under which, at an appropriate diffusion time, the diffusion hierarchy is \emph{topologically equivalent} to the density cluster tree up to a monotone rescaling of heights. It further shows that a $k$-NN graph implementation inherits this equivalence from uniform approximation of diffusion distances, yielding a finite-sample reduction from spectral graph constructions to density-level-set topology.

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