Intrinsic Interleaving Distance for Merge Trees

A merge tree is a type of graph-based topological summary that tracks the evolution of connected components in the sublevel sets of scalar functions. Merge trees enjoy widespread applications in data analysis and scientific visualization. In this paper, we consider the problem of comparing two merge trees via the notion of interleaving distance in the metric space setting. We investigate several theoretical properties of such a metric. In particular, we show that the interleaving distance is intrinsic on the space of labeled merge trees and provide an algorithm to construct metric 1-centers for collections of labeled merge trees. We further prove that the intrinsic property of the interleaving distance also holds for the space of unlabeled merge trees. Our results provide practical recipes for performing statistics on merge trees.