Jona Dirks, Enna Gerhard, Mario Grobler, Amer E. Mouawad, Sebastian Siebertz
{"title":"Data reduction for directed feedback vertex set on graphs without long induced cycles","authors":"Jona Dirks, Enna Gerhard, Mario Grobler, Amer E. Mouawad, Sebastian Siebertz","doi":"10.1007/s00236-025-00490-2","DOIUrl":null,"url":null,"abstract":"<div><p>We study reduction rules for <span>Directed Feedback Vertex Set (DFVS)</span> on directed graphs without long cycles. A <span>DFVS</span> instance without cycles longer than <i>d</i> naturally corresponds to an instance of <i>d</i><span>-Hitting Set</span>, however, enumerating all cycles in an <i>n</i>-vertex graph and then kernelizing the resulting <i>d</i><span>-Hitting Set</span> instance can be too costly, as already enumerating all cycles can take time <span>\\(\\Omega (n^d)\\)</span>. To the best of our knowledge, the kernelization of <span>DFVS</span> on graphs without long cycles has not been studied in the literature, except for very restricted cases, e.g., for tournaments, in which all induced cycles are of length three. We show that the natural reduction rule to delete all vertices and edges that do not lie on induced cycles cannot be implemented efficiently, that is, it is <i>W</i>[1]-hard (with respect to parameter <i>d</i>) to decide if a vertex or edge lies on an induced cycle of length at most <i>d</i> even on graphs that become acyclic after the deletion of a single vertex or edge. Based on different reduction rules we then show how to compute a kernel with at most <span>\\(2^dk^d\\)</span> vertices and at most <span>\\(d^{3d}k^d\\)</span> induced cycles of length at most <i>d</i> (which however, cannot be enumerated efficiently), where <i>k</i> is the size of a minimum directed feedback vertex set. We then study classes of graphs whose underlying undirected graphs have bounded expansion or are nowhere dense. These are very general classes of sparse graphs, containing e.g. classes excluding a minor or a topological minor. We prove that for every class <span>\\(\\mathscr {C} \\)</span> with bounded expansion there is a function <span>\\(f_\\mathscr {C} (d)\\)</span> such that for graphs <span>\\(G\\in \\mathscr {C} \\)</span> without induced cycles of length greater than <i>d</i> we can compute a kernel with <span>\\(f_\\mathscr {C} (d)\\cdot k\\)</span> vertices in time <span>\\(f_\\mathscr {C} (d)\\cdot n^{\\mathcal {O}(1)}\\)</span>. For every nowhere dense class <span>\\(\\mathscr {C} \\)</span> there is a function <span>\\(f_\\mathscr {C} (d,\\varepsilon )\\)</span> such that for graphs <span>\\(G\\in \\mathscr {C} \\)</span> without induced cycles of length greater than <i>d</i> we can compute a kernel with <span>\\(f_\\mathscr {C} (d,\\varepsilon )\\cdot k^{1+\\varepsilon }\\)</span> vertices for any <span>\\(\\varepsilon >0\\)</span> in time <span>\\(f_\\mathscr {C} (d,\\varepsilon )\\cdot n^{\\mathcal {O}(1)}\\)</span>. The most restricted classes we consider are strongly connected planar graphs without any (induced or non-induced) long cycles. We show that these classes have treewidth <span>\\(\\mathcal {O}(d)\\)</span> and hence <span>DFVS</span> on planar graphs without cycles of length greater than <i>d</i> can be solved in time <span>\\(2^{\\mathcal {O}(d)}\\cdot n^{\\mathcal {O}(1)}\\)</span>. We finally present a new data reduction rule for general <span>DFVS</span> and prove that the rule together with a few standard rules subsumes all rules applied in the work of Bergougnoux et al. to obtain a polynomial kernel for <span>DFVS[FVS]</span>, i.e., <span>DFVS</span> parameterized by the feedback vertex set number of the underlying (undirected) graph. We conclude by studying the LP-based approximation of <span>DFVS</span>.</p></div>","PeriodicalId":7189,"journal":{"name":"Acta Informatica","volume":"62 2","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00236-025-00490-2.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Informatica","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s00236-025-00490-2","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
We study reduction rules for Directed Feedback Vertex Set (DFVS) on directed graphs without long cycles. A DFVS instance without cycles longer than d naturally corresponds to an instance of d-Hitting Set, however, enumerating all cycles in an n-vertex graph and then kernelizing the resulting d-Hitting Set instance can be too costly, as already enumerating all cycles can take time \(\Omega (n^d)\). To the best of our knowledge, the kernelization of DFVS on graphs without long cycles has not been studied in the literature, except for very restricted cases, e.g., for tournaments, in which all induced cycles are of length three. We show that the natural reduction rule to delete all vertices and edges that do not lie on induced cycles cannot be implemented efficiently, that is, it is W[1]-hard (with respect to parameter d) to decide if a vertex or edge lies on an induced cycle of length at most d even on graphs that become acyclic after the deletion of a single vertex or edge. Based on different reduction rules we then show how to compute a kernel with at most \(2^dk^d\) vertices and at most \(d^{3d}k^d\) induced cycles of length at most d (which however, cannot be enumerated efficiently), where k is the size of a minimum directed feedback vertex set. We then study classes of graphs whose underlying undirected graphs have bounded expansion or are nowhere dense. These are very general classes of sparse graphs, containing e.g. classes excluding a minor or a topological minor. We prove that for every class \(\mathscr {C} \) with bounded expansion there is a function \(f_\mathscr {C} (d)\) such that for graphs \(G\in \mathscr {C} \) without induced cycles of length greater than d we can compute a kernel with \(f_\mathscr {C} (d)\cdot k\) vertices in time \(f_\mathscr {C} (d)\cdot n^{\mathcal {O}(1)}\). For every nowhere dense class \(\mathscr {C} \) there is a function \(f_\mathscr {C} (d,\varepsilon )\) such that for graphs \(G\in \mathscr {C} \) without induced cycles of length greater than d we can compute a kernel with \(f_\mathscr {C} (d,\varepsilon )\cdot k^{1+\varepsilon }\) vertices for any \(\varepsilon >0\) in time \(f_\mathscr {C} (d,\varepsilon )\cdot n^{\mathcal {O}(1)}\). The most restricted classes we consider are strongly connected planar graphs without any (induced or non-induced) long cycles. We show that these classes have treewidth \(\mathcal {O}(d)\) and hence DFVS on planar graphs without cycles of length greater than d can be solved in time \(2^{\mathcal {O}(d)}\cdot n^{\mathcal {O}(1)}\). We finally present a new data reduction rule for general DFVS and prove that the rule together with a few standard rules subsumes all rules applied in the work of Bergougnoux et al. to obtain a polynomial kernel for DFVS[FVS], i.e., DFVS parameterized by the feedback vertex set number of the underlying (undirected) graph. We conclude by studying the LP-based approximation of DFVS.
期刊介绍:
Acta Informatica provides international dissemination of articles on formal methods for the design and analysis of programs, computing systems and information structures, as well as related fields of Theoretical Computer Science such as Automata Theory, Logic in Computer Science, and Algorithmics.
Topics of interest include:
• semantics of programming languages
• models and modeling languages for concurrent, distributed, reactive and mobile systems
• models and modeling languages for timed, hybrid and probabilistic systems
• specification, program analysis and verification
• model checking and theorem proving
• modal, temporal, first- and higher-order logics, and their variants
• constraint logic, SAT/SMT-solving techniques
• theoretical aspects of databases, semi-structured data and finite model theory
• theoretical aspects of artificial intelligence, knowledge representation, description logic
• automata theory, formal languages, term and graph rewriting
• game-based models, synthesis
• type theory, typed calculi
• algebraic, coalgebraic and categorical methods
• formal aspects of performance, dependability and reliability analysis
• foundations of information and network security
• parallel, distributed and randomized algorithms
• design and analysis of algorithms
• foundations of network and communication protocols.