# Sensitive Distance and Reachability Oracles for Large Batch Updates

arXiv: https://arxiv.org/abs/1907.07982 · HTML: https://mlsanity.com/paper/1907.07982/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Connectivity Oracles for Predictable Vertex Failures](https://mlsanity.com/paper/2312.08489/) (2023)

## Abstract (arXiv)

In the sensitive distance oracle problem, there are three phases. We first preprocess a given directed graph G with n nodes and integer weights from [-W,W]. Second, given a single batch of f edge insertions and deletions, we update the data structure. Third, given a query pair of nodes (u,v), return the distance from u to v. In the easier problem called sensitive reachability oracle problem, we only ask if there exists a directed path from u to v. Our first result is a sensitive distance oracle with tildeO(Wnω+(3-ω)μ) preprocessing time, tildeO(Wn2-μf2+Wnfω) update time, and tildeO(Wn2-μf+Wnf2) query time where the parameter μ∈[0,1] can be chosen. The data-structure requires O(Wn2+μ log n) bits of memory. This is the first algorithm that can handle f≥log n updates. Previous results (e.g. [Demetrescu et al. SICOMP'08; Bernstein and Karger SODA'08 and FOCS'09; Duan and Pettie SODA'09; Grandoni and Williams FOCS'12]) can handle at most 2 updates. When 3≤ f≤log n, the only non-trivial algorithm was by [Weimann and Yuster FOCS'10]. When W=tildeO(1), our algorithm simultaneously improves their preprocessing time, update time, and query time. In particular, when f=ω(1), their update and query time is Omega(n2-o(1)), while our update and query time are truly subquadratic in n, i.e., ours is faster by a polynomial factor of n. To highlight the technique, ours is the first graph algorithm that exploits the kernel basis decomposition of polynomial matrices by [Jeannerod and Villard J.Comp'05; Zhou, Labahn and Storjohann J.Comp'15] developed in the symbolic computation community. [...]