A Dividing Line for Structural Kernelization of Component Order Connectivity via Distance to Bounded Pathwidth
Jakob Greilhuber, Roohani Sharma

TL;DR
This paper investigates the kernelization complexity of the Component Order Connectivity problem, identifying a key structural parameter (distance to pathwidth-1 graphs) that allows polynomial kernelization, unlike the case for pathwidth-2.
Contribution
It establishes that COC admits a polynomial kernel when parameterized by distance to pathwidth-1 graphs plus the component size d, defining a dividing line for kernelization.
Findings
Polynomial kernel for COC with distance to pathwidth-1 graphs plus d.
Kernelization barrier for distance to pathwidth-2 graphs.
Comparison with Vertex Cover kernelization results.
Abstract
In this work we study a classic generalization of the Vertex Cover (VC) problem, called the Component Order Connectivity (COC) problem. In COC, given an undirected graph , integers and , the goal is to determine if there is a set of at most vertices whose deletion results in a graph where each connected component has at most vertices. When , this is exactly VC. This work is inspired by polynomial kernelization results with respect to structural parameters for VC. On one hand, Jansen & Bodlaender [TOCS 2013] show that VC admits a polynomial kernel when the parameter is the distance to treewidth- graphs, on the other hand Cygan, Lokshtanov, Pilipczuk, Pilipczuk & Saurabh [TOCS 2014] showed that VC does not admit a polynomial kernel when the parameter is distance to treewidth- graphs. Greilhuber & Sharma [IPEC 2024] showed that, for any ,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsInterconnection Networks and Systems · Advanced Graph Theory Research · Complexity and Algorithms in Graphs
