18 research outputs found

    Fast Biclustering by Dual Parameterization

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    We study two clustering problems, Starforest Editing, the problem of adding and deleting edges to obtain a disjoint union of stars, and the generalization Bicluster Editing. We show that, in addition to being NP-hard, none of the problems can be solved in subexponential time unless the exponential time hypothesis fails. Misra, Panolan, and Saurabh (MFCS 2013) argue that introducing a bound on the number of connected components in the solution should not make the problem easier: In particular, they argue that the subexponential time algorithm for editing to a fixed number of clusters (p-Cluster Editing) by Fomin et al. (J. Comput. Syst. Sci., 80(7) 2014) is an exception rather than the rule. Here, p is a secondary parameter, bounding the number of components in the solution. However, upon bounding the number of stars or bicliques in the solution, we obtain algorithms which run in time 25pk+O(n+m)2^{5 \sqrt{pk}} + O(n+m) for p-Starforest Editing and 2O(pklog(pk))+O(n+m)2^{O(p \sqrt{k} \log(pk))} + O(n+m) for p-Bicluster Editing. We obtain a similar result for the more general case of t-Partite p-Cluster Editing. This is subexponential in k for fixed number of clusters, since p is then considered a constant. Our results even out the number of multivariate subexponential time algorithms and give reasons to believe that this area warrants further study.Comment: Accepted for presentation at IPEC 201

    Exploring Subexponential Parameterized Complexity of Completion Problems

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    Let F{\cal F} be a family of graphs. In the F{\cal F}-Completion problem, we are given a graph GG and an integer kk as input, and asked whether at most kk edges can be added to GG so that the resulting graph does not contain a graph from F{\cal F} as an induced subgraph. It appeared recently that special cases of F{\cal F}-Completion, the problem of completing into a chordal graph known as Minimum Fill-in, corresponding to the case of F={C4,C5,C6,}{\cal F}=\{C_4,C_5,C_6,\ldots\}, and the problem of completing into a split graph, i.e., the case of F={C4,2K2,C5}{\cal F}=\{C_4, 2K_2, C_5\}, are solvable in parameterized subexponential time 2O(klogk)nO(1)2^{O(\sqrt{k}\log{k})}n^{O(1)}. The exploration of this phenomenon is the main motivation for our research on F{\cal F}-Completion. In this paper we prove that completions into several well studied classes of graphs without long induced cycles also admit parameterized subexponential time algorithms by showing that: - The problem Trivially Perfect Completion is solvable in parameterized subexponential time 2O(klogk)nO(1)2^{O(\sqrt{k}\log{k})}n^{O(1)}, that is F{\cal F}-Completion for F={C4,P4}{\cal F} =\{C_4, P_4\}, a cycle and a path on four vertices. - The problems known in the literature as Pseudosplit Completion, the case where F={2K2,C4}{\cal F} = \{2K_2, C_4\}, and Threshold Completion, where F={2K2,P4,C4}{\cal F} = \{2K_2, P_4, C_4\}, are also solvable in time 2O(klogk)nO(1)2^{O(\sqrt{k}\log{k})} n^{O(1)}. We complement our algorithms for F{\cal F}-Completion with the following lower bounds: - For F={2K2}{\cal F} = \{2K_2\}, F={C4}{\cal F} = \{C_4\}, F={P4}{\cal F} = \{P_4\}, and F={2K2,P4}{\cal F} = \{2K_2, P_4\}, F{\cal F}-Completion cannot be solved in time 2o(k)nO(1)2^{o(k)} n^{O(1)} unless the Exponential Time Hypothesis (ETH) fails. Our upper and lower bounds provide a complete picture of the subexponential parameterized complexity of F{\cal F}-Completion problems for F{2K2,C4,P4}{\cal F}\subseteq\{2K_2, C_4, P_4\}.Comment: 32 pages, 16 figures, A preliminary version of this paper appeared in the proceedings of STACS'1

    A survey of parameterized algorithms and the complexity of edge modification

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    The survey is a comprehensive overview of the developing area of parameterized algorithms for graph modification problems. It describes state of the art in kernelization, subexponential algorithms, and parameterized complexity of graph modification. The main focus is on edge modification problems, where the task is to change some adjacencies in a graph to satisfy some required properties. To facilitate further research, we list many open problems in the area.publishedVersio

    Computing complexity measures of degenerate graphs

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    We show that the VC-dimension of a graph can be computed in time nlogd+1dO(d)n^{\log d+1} d^{O(d)}, where dd is the degeneracy of the input graph. The core idea of our algorithm is a data structure to efficiently query the number of vertices that see a specific subset of vertices inside of a (small) query set. The construction of this data structure takes time O(d2dn)O(d2^dn), afterwards queries can be computed efficiently using fast M\"obius inversion. This data structure turns out to be useful for a range of tasks, especially for finding bipartite patterns in degenerate graphs, and we outline an efficient algorithms for counting the number of times specific patterns occur in a graph. The largest factor in the running time of this algorithm is O(nc)O(n^c), where cc is a parameter of the pattern we call its left covering number. Concrete applications of this algorithm include counting the number of (non-induced) bicliques in linear time, the number of co-matchings in quadratic time, as well as a constant-factor approximation of the ladder index in linear time. Finally, we supplement our theoretical results with several implementations and run experiments on more than 200 real-world datasets -- the largest of which has 8 million edges -- where we obtain interesting insights into the VC-dimension of real-world networks.Comment: Accepted for publication in the 18th International Symposium on Parameterized and Exact Computation (IPEC 2023

    On the number of types in sparse graphs

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    We prove that for every class of graphs C\mathcal{C} which is nowhere dense, as defined by Nesetril and Ossona de Mendez, and for every first order formula ϕ(xˉ,yˉ)\phi(\bar x,\bar y), whenever one draws a graph GCG\in \mathcal{C} and a subset of its nodes AA, the number of subsets of AyˉA^{|\bar y|} which are of the form {vˉAyˉ ⁣:Gϕ(uˉ,vˉ)}\{\bar v\in A^{|\bar y|}\, \colon\, G\models\phi(\bar u,\bar v)\} for some valuation uˉ\bar u of xˉ\bar x in GG is bounded by O(Axˉ+ϵ)\mathcal{O}(|A|^{|\bar x|+\epsilon}), for every ϵ>0\epsilon>0. This provides optimal bounds on the VC-density of first-order definable set systems in nowhere dense graph classes. We also give two new proofs of upper bounds on quantities in nowhere dense classes which are relevant for their logical treatment. Firstly, we provide a new proof of the fact that nowhere dense classes are uniformly quasi-wide, implying explicit, polynomial upper bounds on the functions relating the two notions. Secondly, we give a new combinatorial proof of the result of Adler and Adler stating that every nowhere dense class of graphs is stable. In contrast to the previous proofs of the above results, our proofs are completely finitistic and constructive, and yield explicit and computable upper bounds on quantities related to uniform quasi-wideness (margins) and stability (ladder indices)

    Two-sets cut-uncut on planar graphs

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    We study the following Two-Sets Cut-Uncut problem on planar graphs. Therein, one is given an undirected planar graph GG and two sets of vertices SS and TT. The question is, what is the minimum number of edges to remove from GG, such that we separate all of SS from all of TT, while maintaining that every vertex in SS, and respectively in TT, stays in the same connected component. We show that this problem can be solved in time 2S+TnO(1)2^{|S|+|T|} n^{O(1)} with a one-sided error randomized algorithm. Our algorithm implies a polynomial-time algorithm for the network diversion problem on planar graphs, which resolves an open question from the literature. More generally, we show that Two-Sets Cut-Uncut remains fixed-parameter tractable even when parameterized by the number rr of faces in the plane graph covering the terminals STS \cup T, by providing an algorithm of running time 4r+O(r)nO(1)4^{r + O(\sqrt r)} n^{O(1)}.Comment: 22 pages, 5 figure

    Kernelization and Sparseness: the case of Dominating Set

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    We prove that for every positive integer rr and for every graph class G\mathcal G of bounded expansion, the rr-Dominating Set problem admits a linear kernel on graphs from G\mathcal G. Moreover, when G\mathcal G is only assumed to be nowhere dense, then we give an almost linear kernel on G\mathcal G for the classic Dominating Set problem, i.e., for the case r=1r=1. These results generalize a line of previous research on finding linear kernels for Dominating Set and rr-Dominating Set. However, the approach taken in this work, which is based on the theory of sparse graphs, is radically different and conceptually much simpler than the previous approaches. We complement our findings by showing that for the closely related Connected Dominating Set problem, the existence of such kernelization algorithms is unlikely, even though the problem is known to admit a linear kernel on HH-topological-minor-free graphs. Also, we prove that for any somewhere dense class G\mathcal G, there is some rr for which rr-Dominating Set is W[22]-hard on G\mathcal G. Thus, our results fall short of proving a sharp dichotomy for the parameterized complexity of rr-Dominating Set on subgraph-monotone graph classes: we conjecture that the border of tractability lies exactly between nowhere dense and somewhere dense graph classes.Comment: v2: new author, added results for r-Dominating Sets in bounded expansion graph

    Parameterized Graph Modification Algorithms

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    Graph modification problems form an important class of algorithmic problems in computer science. In this thesis, we study edge modification problems towards classes related to chordal graphs, with the main focus on trivially perfect graphs and threshold graphs. We provide several new results in classical complexity, kernelization complexity, and subexponential parameterized complexity. In all cases we give positive and negative results—giving polynomial time algorithms as well as NP-hardness results, polynomial kernels as well as polynomial kernel impossibility results, and we give subexponential time algorithms, and show that many problems do not admit such algorithms unless the exponential time hypothesis fails. Our main focus is on the subexponential time complexity of edge modification problems. For that to make sense, we first need to figure out whether or not we actually need super-polynomial time. We show that editing towards trivially perfect graphs, threshold graphs, and chain graphs are all NP-complete, resolving 15 year old open questions. When a problem is shown to be NP-complete, we study exactly how much exponential time is needed for an algorithm to solve it. We provide several subexponential time algorithms, for, e.g., editing towards chain graphs and threshold graphs, as well as completing towards trivially perfect graphs. We complement our results by showing that small alterations in the target graph classes yields much harder problems: Editing towards trivially perfect graphs and cographs is not possible in subexponential time unless the exponential time hypothesis fails. A first step in our subexponential time algorithms, and an otherwise natural first step in dealing with NP-hard problems is offered by the toolbox of polynomial kernelization. In polynomial kernelizations, we are asked to design polynomial time compression algorithms that shrink the input instances to output instances bounded polynomially in a yes-solution. We provide polynomial kernels for all edge modification problems towards trivially perfect graphs, threshold graphs and chain graphs. In addition, we show that on bounded degree input graphs, we obtain polynomial kernels for any editing or deletion problem towards graph classes characterizable by a finite set of forbidden induced subgraphs. Finally, we show that we should not expect the same result for completion problems by proving that such a compression algorithm would imply the collapse of the polynomial hierarchy
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