  We investigate properties of node centrality in random growing tree models.
We focus on a measure of centrality that computes the maximum subtree size of
the tree rooted at each node, with the most central node being the tree
centroid. For random trees grown according to a preferential attachment model,
a uniform attachment model, or a diffusion processes over a regular tree, we
prove that a single node persists as the tree centroid after a finite number of
steps, with probability 1. Furthermore, this persistence property generalizes
to the top $K \ge 1$ nodes with respect to the same centrality measure. We also
establish necessary and sufficient conditions for the size of an initial seed
graph required to ensure persistence of a particular node with probability
$1-\epsilon$, as a function of $\epsilon$: In the case of preferential and
uniform attachment models, we derive bounds for the size of an initial hub
constructed around the special node. In the case of a diffusion process over a
regular tree, we derive bounds for the radius of an initial ball centered
around the special node. Our necessary and sufficient conditions match up to
constant factors for preferential attachment and diffusion tree models.
