  Diffusion state distance (DSD) is a metric on the vertices of a graph,
motivated by bioinformatic modeling. Previous results on the convergence of DSD
to a limiting metric relied on the definition being based on symmetric or
reversible random walk on the graph. We show that convergence holds even when
the DSD is based on general finite irreducible Markov chains. The proofs rely
on classical potential theory of Kemeny and Snell.
