Computing the Interleaving Distance is NP-Hard

Bjerkevik, Håvard Bakke; Botnan, Magnus Bakke; Kerber, Michael

doi:10.1007/s10208-019-09442-y

Cited by 25 publications

(26 citation statements)

References 22 publications

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“…Consider any two constant DMSs γ X ≡ (X , d X ) and γ Y ≡ (Y , d Y ). Then, for any k ∈ Z + , inequality (7) reduces to:…”

Section: Relationship With Standard Stability Theoremsmentioning

confidence: 99%

“…Given δ > 0, deciding whether d Vec I (M , N ) ≤ δ is in general NP-hard [7,8]. In Theorem 6.2, substituting the comparison of M and N with that of rk(M ) and rk(N ) results in doubling of the underlying dimension of the interleaving distance.…”

Section: Given Anymentioning

confidence: 99%

“…Next, in regard to item (b) above, we address the issue of computability of the metric between invariants of DMSs. In [7,8], Bjerkevik and Botnan show that computing the interleaving distance d I [52] between multidimensional persistence modules can in general be NP-hard. Also, since we are not guaranteed to have interval decomposability [9,17] of the 3-dimensional modules considered in this paper, we are not in a position to utilize the bottleneck distance and relevant algorithms developed by Dey and Xin [28] instead of d I .…”

Section: Introductionmentioning

confidence: 99%

“…We specifically emphasize that our stability results are a generalization of the well known stability theorems for the SLHC method [16] and the Rips filtration of a metric space [22,23]: Indeed, we show that by restricting ourselves to the class of constant DMSs, our results reduce to the standard stability theorems for static metric spaces in [16,22,23].Next, in regard to item (b) above, we address the issue of computability of the metric between invariants of DMSs. In [7,8], Bjerkevik and Botnan show that computing the interleaving distance d I [52] between multidimensional persistence modules can in general be NP-hard. Also, since we are not guaranteed to have interval decomposability [9,17] of the 3-dimensional modules considered in this paper, we are not in a position to utilize the bottleneck distance and relevant algorithms developed by Dey and Xin [28] instead of d I .This motivates us to further simplify our invariant M X associated to a DMS (X , d X (·)), which is in the form of 3-dimensional persistence module.…”

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confidence: 99%

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Spatiotemporal Persistent Homology for Dynamic Metric Spaces

Kim

Mémoli

2020

Discrete Comput Geom

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Characterizing the dynamics of time-evolving data within the framework of topological data analysis (TDA) has been attracting increasingly more attention. Popular instances of time-evolving data include flocking/swarming behaviors in animals and social networks in the human sphere. A natural mathematical model for such collective behaviors is a dynamic point cloud, or more generally a dynamic metric space (DMS).In this paper we extend the Rips filtration stability result for (static) metric spaces to the setting of DMSs. We do this by devising a certain three-parameter "spatiotemporal" filtration of a DMS. Applying the homology functor to this filtration gives rise to multidimensional persistence module derived from the DMS. We show that this multidimensional module enjoys stability under a suitable generalization of the Gromov-Hausdorff distance which permits metrizing the collection of all DMSs.On the other hand, it is recognized that, in general, comparing two multidimensional persistence modules leads to intractable computational problems. For the purpose of practical comparison of DMSs, we focus on both the rank invariant or the dimension function of the multidimensional persistence module that is derived from a DMS. We specifically propose to utilize a certain metric d for comparing these invariants: In our work this d is either (1) a certain generalization of the erosion distance by Patel, or (2) a specialized version of the well known interleaving distance. In either case, the metric d can be computed in polynomial time. IntroductionStability and tractability of TDA for studying metric spaces. Finite point clouds or finite metric spaces are amongst the most common data representations considered in topological data analysis (TDA) [13,29,33]. In particular, the stability of the Single Linkage Hierarchical Clustering (SLHC) method [16] or the stability of the persistent homology of filtered Rips complexes built on metric spaces [22,23] motivates adopting these constructions when studying metric spaces arising in applications.Whereas there has been extensive applications of TDA to static metric data (thanks to the aforementioned theoretical underpinnings), there is not much study of dynamic metric data from the TDA perspective. Our motivation for considering dynamic metric data stems from the study and characterization of flocking/swarming behaviors of animals [5,36,37,39,53,57,63,69], convoys [41], moving clusters [43], or mobile groups [40,70]. In this paper, by extending ideas from [16,22,23,47,46], we aim at establishing a TDA framework for the study of dynamic metric spaces (DMSs) which comes together with stability theorems. We begin by describing and comparing relevant work with ours. Lack of an adequate metric for DMSs. In [55], Munch considers vineyards -a certain notion of time-varying persistence diagrams introduced by Cohen-Steiner et al. [25] -as signatures for dynamic point clouds. Munch, in particular, shows that vineyards are stable 1 [24] under perturbations of the input dynamic point cloud ...

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“…Consider any two constant DMSs γ X ≡ (X , d X ) and γ Y ≡ (Y , d Y ). Then, for any k ∈ Z + , inequality (7) reduces to:…”

Section: Relationship With Standard Stability Theoremsmentioning

confidence: 99%

Section: Given Anymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Spatiotemporal Persistent Homology for Dynamic Metric Spaces

Kim

Mémoli

2020

Discrete Comput Geom

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“…However, bi-filtrations still admit meaningful distance measures, which lead to the notion of closeness of two bi-filtrations. The most prominent such distance is the interleaving distance [39], which, however, has recently been proved to be NP-complete to compute and approximate [8]. Computationally attractive alternatives are (multi-parameter) bottleneck distance [22] and the matching distance [5,35], which compares the persistence diagrams along all slices (appropriately weighted) and picks the worst discrepancy as the distance of the bi-filtrations.…”

Section: Introductionmentioning

confidence: 99%

A kernel for multi-parameter persistent homology

Corbet

Fugacci

Kerber

et al. 2019

Computers & Graphics: X

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Topological data analysis and its main method, persistent homology, provide a toolkit for computing topological information of high-dimensional and noisy data sets. Kernels for oneparameter persistent homology have been established to connect persistent homology with machine learning techniques with applicability on shape analysis, recognition and classification. We contribute a kernel construction for multi-parameter persistence by integrating a one-parameter kernel weighted along straight lines. We prove that our kernel is stable and efficiently computable, which establishes a theoretical connection between topological data analysis and machine learning for multivariate data analysis.

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Analysis of Dynamic Graphs and Dynamic Metric Spaces via Zigzag Persistence

2020

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When studying flocking/swarming behaviors in animals one is interested in quantifying and comparing the dynamics of the clustering induced by the coalescence and disbanding of animals in different groups. In a similar vein, studying the dynamics of social networks leads to the problem of characterizing groups/communities as they form and disperse throughout time.Motivated by this, we study the problem of obtaining persistent homology based summaries of timedependent data. Given a finite dynamic graph (DG), we first construct a zigzag persistence module arising from linearizing the dynamic transitive graph naturally induced from the input DG. Based on standard results, we then obtain a persistence diagram or barcode from this zigzag persistence module. We prove that these barcodes are stable under perturbations in the input DG under a suitable distance between DGs that we identify.More precisely, our stability theorem can be interpreted as providing a lower bound for the distance between DGs. Since it relies on barcodes, and their bottleneck distance, this lower bound can be computed in polynomial time from the DG inputs.Since DGs can be given rise by applying the Rips functor (with a fixed threshold) to dynamic metric spaces, we are also able to derive related stable invariants for these richer class of dynamic objects.Along the way, we propose a summarization of dynamic graphs that captures their time-dependent clustering features which we call formigrams. These set-valued functions generalize the notion of dendrogram, a prevalent tool for hierarchical clustering. In order to elucidate the relationship between our distance between two DGs and the bottleneck distance between their associated barcodes, we exploit recent advances in the stability of zigzag persistence due to Botnan and Lesnick, and to Bjerkevik.

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Computing the Interleaving Distance is NP-Hard

Cited by 25 publications

References 22 publications

Spatiotemporal Persistent Homology for Dynamic Metric Spaces

Spatiotemporal Persistent Homology for Dynamic Metric Spaces

A kernel for multi-parameter persistent homology

Analysis of Dynamic Graphs and Dynamic Metric Spaces via Zigzag Persistence

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