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Algorithmic Properties of Sparse Digraphs

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Algorithmic Properties of Sparse Digraphs 1 Algorithmic Properties of Sparse Digraphs 1 2 Stephan Kreutzer 3 Technische Universität Berlin, Germany 4 [email protected] 2 5 Irene Muzi 6 University of Warsaw, Poland 7 [email protected] 3 8 Patrice Ossona de Mendez 9 Centre d’Analyse et de Mathématiques Sociales (CNRS, UMR 8557), Paris, France, and 10 Computer Science Institute of Charles University (IUUK), Prague, Czech Republic 11 [email protected] 12 Roman Rabinovich 13 Technische Universität Berlin, Germany 14 [email protected] 4 15 Sebastian Siebertz 16 University of Warsaw, Poland 17 [email protected] 18 19 Abstract 20 The notions of bounded expansion [54] and nowhere denseness [56], introduced by Nešetřil and 21 Ossona de Mendez as structural measures for undirected graphs, have been applied very suc- 22 cessfully in algorithmic graph theory. We study the corresponding notions of directed bounded 23 expansion and nowhere crownfulness on directed graphs, introduced by Kreutzer and Tazari 24 in [46]. These classes are very general classes of sparse directed graphs, as they include, on 25 one hand, all classes of directed graphs whose underlying undirected class has bounded expan- 26 sion, such as planar, bounded-genus, and H-minor-free graphs, and on the other hand, they 27 also contain classes whose underlying undirected class is not even nowhere dense. We show that 28 many of the algorithmic tools that were developed for undirected bounded expansion classes can, 29 with some care, also be applied in their directed counterparts, and thereby we highlight a rich 30 algorithmic structure theory of directed bounded expansion and nowhere crownful classes. 31 2012 ACM Subject Classification 32 Mathematics of computing → Graph algorithms 33 Theory of computation → Fixed parameter tractability 34 Keywords and phrases Directed graphs, graph algorithms, parameterized complexity, approx- 35 imation 36 Digital Object Identifier 10.4230/LIPIcs.STACS.2019.23 1 Supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (ERC consolidator grant DISTRUCT, agreement No. 648527). 2 Supported by ERC starting grant CUTACOMBS grant no 714704. 3 Supported by grant ERCCZ LL-1201 and by the European Associated Laboratory “Structures in Combinatorics” (LEA STRUCO), and partially supported by ANR project Stint under reference ANR-13-BS02-0007. 4 Supported by the National Science Centre of Poland via POLONEZ grant agreement UMO- 2015/19/P/ST6/03998, which has received funding from the European Union’s Horizon 2020 research and innovation programme (Marie Skłodowska-Curie grant agreement No. 665778). S. Kreutzer, I. Muzi, P. Ossona de Mendez, R. Rabinovich, S. Siebertz 1 37 1 Introduction 38 Structural graph theory has made a deep impact on the design of graph algorithms for hard 39 problems. It provides a wealth of different tools for dealing with the intrinsic complexity of NP- 40 hard problems on graphs and these methods have been applied very successfully in algorithmic 41 graph theory, in approximation theory, optimisation and the design of exact and parameterised 42 algorithms for problems on undirected graphs, see e.g. [11, 14, 15, 16, 17, 18, 27, 28, 64]. 43 Concepts such as tree width or excluded (topological) minors as well as density based graph 44 parameters such as bounded expansion or nowhere denseness capture important properties of 45 graphs and make them applicable for algorithmic applications. 46 The notions of bounded expansion and nowhere denseness were introduced in [54] and 47 [56] to capture structural sparseness of undirected graphs. Classes of bounded expansion are 48 very general and properly generalise, for instance, planar graphs or more generally classes 49 with excluded (topological) minors. But the concept goes far beyond excluded minor classes. 50 Starting with[ 54, 56], many algorithmic results for problems on classes of graphs excluding 51 a fixed minor have been extended to the more general case of bounded expansion and nowhere 52 dense classes of graphs, see e.g. [9, 13, 20, 21, 23, 24, 32, 37, 42, 43, 47, 53, 59, 67]. Furthermore, 53 Demaine et al. [19] and Nadara et al. [51] analysed a range of real-world networks and showed 54 that many of them indeed fall within the framework of bounded expansion. This shows that 55 this concept captures many types of real world instances. 56 An important aspect of classes of bounded expansion and classes which are nowhere dense 57 is that they can equivalently be defined in many different and seemingly unrelated ways: 58 by the density of bounded depth minors, by low tree depth colourings [54], by generalised 59 colouring numbers [70], by wideness properties such as uniformly quasi wideness [55], by 60 sparse neighbourhood covers [36, 37], vc-density [60], and many more. Each of these different 61 aspects of the theory comes with its own set of algorithmic tools and many of the more 62 advanced algorithmic results on bounded expansion classes mentioned above crucially rely 63 on a combination of several of these techniques. 64 Developing a structural theory for directed graphs that yields classes of digraphs with 65 a similarly broad algorithmic impact has so far not seen a comparable success as for the 66 undirected case. The general goal is to identify structural parameters which define interesting 67 and general classes of digraphs for which there is a comparably rich set of algorithmic tools. 68 However, essentially all approaches, e.g. in [6, 7, 33, 39, 57, 65], of generalising even the 69 well-understood and fairly basic concept of tree width to digraphs have failed to produce 70 digraph parameters that come even near the wide spectrum of algorithmic applications that 71 tree width has found. This even has led to claims that this programme cannot be successful 72 and that such measures for digraphs cannot exist [34]. 73 In this paper we exhibit examples of digraph parameters which we believe challenge this 74 negative outlook on the potential of digraph parameters. Our main conceptual contribution 75 is to give a positive example of a digraph parameter that satisfies the conditions of the 76 programme outlined above: we identify a very general type of digraph classes which have a 77 similar set of algorithmic tools available as their undirected counterparts. We believe that 78 these classes give a positive answer to the question whether interesting graph parameters can 79 successfully be generalised to the directed setting and we support this claim by algorithmic 80 applications described below. 81 The classes of digraphs we study are classes of directed bounded expansion and nowhere 82 crownful classes of digraphs which are modeled after the concepts of bounded expansion 83 and nowhere denseness for undirected graphs, respectively. They were originally defined 2 Algorithmic Properties of Sparse Digraphs 84 in [46], where basic properties of these classes were developed. In particular, it was shown 85 that nowhere crownfull classes can equivalently be defined in terms of directed uniformly 86 quasi-wideness, analogous to its undirected counterpart, which easily implies fixed-parameter 87 tractability of the directed dominating set problem on these classes. See Section 2 for details. 88 The first improvement of these initial results appeared in [45], where structural properties of 89 classes of digraphs of bounded expansion were studied. The main contribution of [45] was to 90 establish their relation to a certain form of generalised colouring numbers, a concept which 91 in the undirected setting has had huge algorithmic impact on the development of algorithms 92 for nowhere dense and bounded expansion classes. 93 Our contributions. These initial results are the starting point for our investigation in 94 this paper. In addition to directed bounded expansion and nowhere crownful classes, we also 95 define a new type of digraph classes which we call bounded crownless expansion. 96 Our main contributions are both structural and algorithmic. We show that classes 97 of digraphs of directed bounded expansion, and especially classes of bounded crownless 98 expansion, have structural properties very similar to their undirected counterpart. As a 99 consequence, we are able to show that many of the algorithmic tools that were developed 100 for undirected bounded expansion have their directed counterpart resulting in a rich and 101 diverse set of algorithmic techniques that can be applied in the design of algorithms for 102 these classes. To the best of our knowledge, this is the first time that the generalisation 103 of one of the widely studied and very general undirected graph parameter to the digraph 104 setting has indeed led to a digraph concept with a similarly broad set of algorithmic tools 105 as its undirected counterpart. We are therefore optimistic that classes of directed bounded 106 expansion or crownless expansion will find a broad range of applications. We support this 107 belief by providing several algorithmic results we describe next. 108 As a test case for these algorithmic techniques we use the directed variant of the 109 (Distance-r) Dominating Set problem defined as follows. For a positive integer r, 110 a distance-r dominating set in a digraph G is set D ⊆ V (G) such that every v ∈ V (G) is + 111 reachable by a directed path of length at most r from a vertex d ∈ D, i.e. Nr (D) = V (G). 112 (Distance-r) Dominating Set is a common benchmark problem for the design of 113 (parameterised or approximation) algorithms on graph classes with structural restrictions. 114 It is NP-complete in general [41], and (under standard complexity theoretical assumptions) 115 cannot be approximated better than up to a factor O(log n) [62]. Better results can be 116 achieved, e.g., on sparse graph classes, see e.g.
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