Abstract
This work introduces the development of path Dirac and hypergraph Dirac operators, along with an exploration of their persistence. These operators excel in distinguishing between harmonic and non-harmonic spectra, offering valuable insights into the subcomplexes within these structures. The paper showcases the functionality of these operators through a series of examples in various contexts. An essential facet of this research involves examining the operators' sensitivity to filtration, emphasizing their capacity to adapt to topological changes. The paper also explores a significant application of persistent path Dirac and persistent hypergraph Dirac in molecular science, specifically in analyzing molecular structures. The study introduces strict preorders derived from molecular structures, which generate graphs and digraphs with intricate path structures. The depth of information within these path complexes reflects the complexity of different preorder classes influenced by molecular structures. This characteristic underscores the effectiveness of these tools in the realm of topological data analysis.
| Original language | English |
|---|---|
| Pages (from-to) | 124-153 |
| Number of pages | 30 |
| Journal | Foundations of Data Science |
| Volume | 6 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 2024 |
Bibliographical note
Publisher Copyright:© 2024 American Institute of Mathematical Sciences. All rights reserved.
Keywords
- Persistent hypergraph Dirac
- persistent digraph Dirac
- simultaneous geometric
- spectral data analysis
- topological analyses
- topological data analysis
ASJC Scopus subject areas
- Analysis
- Statistics and Probability
- Computational Theory and Mathematics
- Applied Mathematics
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