Pattern-Sparse Tree Decompositions in $H$-Minor-Free Graphs
This provides algorithmic tools for solving pattern-finding problems in graph classes like H-minor-free graphs, extending prior work to handle disconnected patterns and neighborhood constraints, which is incremental but broadens applicability.
The paper tackles the problem of finding tree decompositions for H-minor-free graphs that sparsely contain patterns of size k, achieving a randomized polynomial-time algorithm that yields width O~(k) and ensures each bag contains at most O~(√k) vertices of the pattern with probability at least (2^{O~(√k)}n^{O(1)})^{-1, enabling solutions to problems like Directed k-Path in time 2^{O~(√k)}n^{O(1)}.
Given an $H$-minor-free graph $G$ and an integer $k$, our main technical contribution is sampling in randomized polynomial time an induced subgraph $G'$ of $G$ and a tree decomposition of $G'$ of width $\widetilde{O}(k)$ such that for every $Z\subseteq V(G)$ of size $k$, with probability at least $\left(2^{\widetilde{O}(\sqrt{k})}|V(G)|^{O(1)}\right)^{-1}$, we have $Z \subseteq V(G')$ and every bag of the tree decomposition contains at most $\widetilde{O}(\sqrt{k})$ vertices of $Z$. Having such a tree decomposition allows us to solve a wide range of problems in (randomized) time $2^{\widetilde{O}(\sqrt{k})}n^{O(1)}$ where the solution is a pattern $Z$ of size $k$, e.g., Directed $k$-Path, $H$-Packing, etc. In particular, our result recovers all the algorithmic applications of the pattern-covering result of Fomin et al. [SIAM J. Computing 2022] (which requires the pattern to be connected) and the planar subgraph-finding algorithms of Nederlof [STOC 2020]. Furthermore, for $K_{h,3}$-free graphs (which include bounded-genus graphs) and for a fixed constant $d$, we signficantly strengthen the result by ensuring that not only $Z$ has intersection $\widetilde{O}(\sqrt{k})$ with each bag, but even the distance-$d$ neighborhood $N^d_{G}[Z]$ as well. This extension makes it possible to handle a wider range of problems where the neighborhood of the pattern also plays a role in the solution, such as partial domination problems and problems involving distance constraints.