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Revisiting Graph Persistence for Updates and Efficiency

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Algorithms and Data Structures (WADS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14079))

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Abstract

It is well known that ordinary persistence on graphs can be computed more efficiently than the general persistence. Recently, it has been shown that zigzag persistence on graphs also exhibits similar behavior. Motivated by these results, we revisit graph persistence and propose efficient algorithms especially for local updates on filtrations, similar to what is done in ordinary persistence for computing the vineyard. We show that, for a filtration of length m, (i) switches (transpositions) in ordinary graph persistence can be done in \(O(\log m)\) time; (ii) zigzag persistence on graphs can be computed in \(O(m\log m)\) time, which improves a recent \(O(m\log ^4n)\) time algorithm assuming n, the size of the union of all graphs in the filtration, satisfies \(n\in \varOmega ({m^\varepsilon })\) for any fixed \(0<\varepsilon <1\); (iii) open-closed, closed-open, and closed-closed bars in dimension 0 for graph zigzag persistence can be updated in \(O(\log m)\) time, whereas the open-open bars in dimension 0 and closed-closed bars in dimension 1 can be done in \(O(\sqrt{m}\,\log m)\) time.

This work is partially supported by NSF grant CCF 2049010.

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Notes

  1. 1.

    https://arxiv.org/pdf/2302.12796.pdf.

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Correspondence to Tamal K. Dey , Tao Hou or Salman Parsa .

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Dey, T.K., Hou, T., Parsa, S. (2023). Revisiting Graph Persistence for Updates and Efficiency. In: Morin, P., Suri, S. (eds) Algorithms and Data Structures. WADS 2023. Lecture Notes in Computer Science, vol 14079. Springer, Cham. https://doi.org/10.1007/978-3-031-38906-1_24

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  • DOI: https://doi.org/10.1007/978-3-031-38906-1_24

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