Intrinsic Interleaving Distance for Merge Trees

Ellen Gasparovic (1) , Elizabeth Munch (2) , Steve Y. Oudot (3) , Katharine Turner (4) , Bei Wang (5) , Yusu Wang (6)

1 Union College
2 Michigan State University System
3 DATASHAPE - Understanding the Shape of Data
4 BMI - EPFL - Brain Mind Institute
5 Zhejiang University
6 OSU - Ohio State University [Columbus]

Merge trees are a type of graph-based topological summary that tracks the evolution of connected components in the sublevel sets of scalar functions. They 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 various 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 are a first step toward performing statistics on graph-based topological summaries.

Domaines: Géométrie algorithmique [cs.CG]

Ellen Gasparovic, Elizabeth Munch, Steve Y. Oudot, Katharine Turner, Bei Wang, et al.. Intrinsic Interleaving Distance for Merge Trees. 2019. ⟨hal-02425600⟩