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Intrinsic Interleaving Distance for Merge Trees

Intrinsic interleaving distance for merge trees
Authors: Ellen Gasparovic; Elizabeth Munch; Steve Oudot; Katharine Turner; Bei Wang; Yusu Wang;

Intrinsic Interleaving Distance for Merge Trees

Abstract

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.

Keywords

Computational Geometry (cs.CG), FOS: Computer and information sciences, Graph operations (line graphs, products, etc.), [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], Computing methodologies for image processing, 004, topological data analysis, [INFO.INFO-CG] Computer Science [cs]/Computational Geometry [cs.CG], merge tree, FOS: Mathematics, Computer Science - Computational Geometry, Algebraic Topology (math.AT), Mathematics - Algebraic Topology, interleaving distance

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Top 10%
Green