Computing Minimal Persistent Cycles: Polynomial and Hard Cases
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Abstract (arXiv)
Persistent cycles, especially the minimal ones, are useful geometric features functioning as augmentations for the intervals in a purely topological persistence diagram (also termed as barcode). In our earlier work, we showed that computing minimal 1-dimensional persistent cycles (persistent 1-cycles) for finite intervals is NP-hard while the same for infinite intervals is polynomially tractable. In this paper, we address this problem for general dimensions with mathbbZ2 coefficients. In addition to proving that it is NP-hard to compute minimal persistent d-cycles (d>1) for both types of intervals given arbitrary simplicial complexes, we identify two interesting cases which are polynomially tractable. These two cases assume the complex to be a certain generalization of manifolds which we term as weak pseudomanifolds. For finite intervals from the d-th persistence diagram of a weak (d+1)-pseudomanifold, we utilize the fact that persistent cycles of such intervals are null-homologous and reduce the problem to a minimal cut problem. Since the same problem for infinite intervals is NP-hard, we further assume the weak (d+1)-pseudomanifold to be embedded in ℝd+1 so that the complex has a natural dual graph structure and the problem reduces to a minimal cut problem. Experiments with both algorithms on scientific data indicate that the minimal persistent cycles capture various significant features of the data.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jul 2019.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 1.
4 papers.
- 2021Topological Detection of Trojaned Neural Networkscited by 34
- 2021Cycle Representation Learning for Inductive Relation Predictioncited by 23
- 2021The decomposition of the higher-order homology embedding constructed from the k-Laplaciancited by 8
- 2024Geometric Localization of Homology Cyclescited by 0
- 2024Geometric Localization of Homology Cycles5 Jun 2024
- 2021Cycle Representation Learning for Inductive Relation Prediction6 Oct 2021
- 2021The decomposition of the higher-order homology embedding constructed from the k-Laplacian23 Jul 2021
- 2021Topological Detection of Trojaned Neural Networks11 Jun 2021