Efficient fully dynamic elimination forests with applications to detecting long paths and cycles

TL;DR

This paper introduces a dynamic data structure for graphs with bounded treedepth that efficiently maintains an optimal elimination forest, enabling fast detection of long paths and cycles in changing graphs.

Contribution

It presents a new fully dynamic data structure with worst-case update time matching static algorithms for treedepth, improving previous bounds and applications to path and cycle detection.

Findings

Achieves worst-case update time $2^{O(d^2)}$ for treedepth $d$ graphs.

Improves bounds on minimal obstructions for treedepth from doubly-exponential to $d^{O(d)}$.

Provides dynamic algorithms for detecting long paths and cycles with exponential dependence on parameter $k$.

Abstract

We present a data structure that in a dynamic graph of treedepth at most $d$, which is modified over time by edge insertions and deletions, maintains an optimum-height elimination forest. The data structure achieves worst-case update time $2^{{\cal O}(d^2)}$, which matches the best known parameter dependency in the running time of a static fpt algorithm for computing the treedepth of a graph. This improves a result of Dvo\v{r}\'ak et al. [ESA 2014], who for the same problem achieved update time $f(d)$ for some non-elementary (i.e. tower-exponential) function $f$. As a by-product, we improve known upper bounds on the sizes of minimal obstructions for having treedepth $d$ from doubly-exponential in $d$ to $d^{{\cal O}(d)}$. As applications, we design new fully dynamic parameterized data structures for detecting long paths and cycles in general graphs. More precisely, for a fixed parameter $k$ and a dynamic graph $G$, modified over time by edge insertions and deletions, our data structures maintain answers to the following queries: - Does $G$ contain a simple path on $k$ vertices? - Does $G$ contain a simple cycle on at least $k$ vertices? In the first case, the data structure achieves amortized update time $2^{{\cal O}(k^2)}$. In the second case, the amortized update time is $2^{{\cal O}(k^4)} + {\cal O}(k \log n)$. In both cases we assume access to a dictionary on the edges of $G$.