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A well-studied continuous model of graphs considers each edge as a continuous unit-length interval of points. For $\delta \geq 0$, we introduce the problem $\delta$-Tour, where the objective is to find the shortest tour that comes within a distance of $\delta$ of every point on every edge. It can be observed that 0-Tour is essentially equivalent to the Chinese Postman Problem, which is solvable in polynomial time. In contrast, 1/2-Tour is essentially equivalent to the graphic Traveling Salesman Problem (TSP), which is NP-hard but admits a constant-factor approximation in polynomial time. We investigate $\delta$-Tour for other values of $\delta$, noting that the problem's behavior and the insights required to understand it differ significantly across various $\delta$ regimes. On one hand, we examine the approximability of the problem for every fixed $\delta > 0$: (1) For every fixed $0 < \delta < 3/2$, the problem $\delta$-Tour admits a constant-factor approximation and is APX-hard, while for every fixed $\delta \geq 3/2$, the problem admits an $O(\log n)$-approximation algorithm and has no polynomial-time $o(\log n)$-approximation, unless P=NP. Our techniques also yield a new APX-hardness result for graphic TSP on cubic bipartite graphs. When parameterizing by tour length, it is relatively easy to show that 3/2 is the threshold of fixed-parameter tractability: (2) For every fixed $0 < \delta < 3/2$, the problem $\delta$-Tour is FPT parameterized by tour length but is W[2]-hard for every fixed $\delta \geq 3/2$. On the other hand, if $\delta$ is part of the input, then an interesting phenomenon occurs when $\delta$ is a constant fraction of n: (3) Here, the problem can be solved in time $f(k) n^{O(k)}$, where $k = \lceil n/\delta \rceil$; however, assuming ETH, there is no algorithm that solves the problem in time $f(k) n^{o(k/\log k)}$., Comment: To appear in ISAAC 2024

$\delta$-Covering, for some covering range $\delta>0$, is a continuous facility location problem on undirected graphs where all edges have unit length. The facilities may be positioned on the vertices as well as on the interior of the edges. The goal is to position as few facilities as possible such that every point on every edge has distance at most $\delta$ to one of these facilities. For large $\delta$, the problem is similar to dominating set, which is hard to approximate, while for small $\delta$, say close to $1$, the problem is similar to vertex cover. In fact, as shown by Hartmann et al. [Math. Program. 22], $\delta$-Covering for all unit-fractions $\delta$ is polynomial time solvable, while for all other values of $\delta$ the problem is NP-hard. We study the approximability of $\delta$-Covering for every covering range $\delta>0$. For $\delta \geq 3/2$, the problem is log-APX-hard, and allows an $\mathcal O(\log n)$ approximation. For every $\delta < 3/2$, there is a constant factor approximation of a minimum $\delta$-cover (and the problem is APX-hard when $\delta$ is not a unit-fraction). We further study the dependency of the approximation ratio on the covering range $\delta < 3/2$. By providing several polynomial time approximation algorithms and lower bounds under the Unique Games Conjecture, we narrow the possible approximation ratio, especially for $\delta$ close to the polynomial time solvable cases., Comment: 22 pages, 6 figures, extended abstract accepted at WAOA24

A directed graph $D$ is singly connected if for every ordered pair of vertices $(s,t)$, there is at most one path from $s$ to $t$ in $D$. Graph orientation problems ask, given an undirected graph $G$, to find an orientation of the edges such that the resultant directed graph $D$ has a certain property. In this work, we study the graph orientation problem where the desired property is that $D$ is singly connected. Our main result concerns graphs of a fixed girth $g$ and coloring number $c$. For every $g,c\geq 3$, the problem restricted to instances of girth $g$ and coloring number $c$, is either NP-complete or in P. As further algorithmic results, we show that the problem is NP-hard on planar graphs and polynomial time solvable distance-hereditary graphs.

In 1992 Bir\'{o}, Hujter and Tuza introduced, for every fixed connected graph $H$, the class of $H$-graphs, defined as the intersection graphs of connected subgraphs of some subdivision of $H$. Recently, quite a lot of research has been devoted to understanding the tractability border for various computational problems, such as recognition or isomorphism testing, in classes of $H$-graphs for different graphs $H$. In this work we undertake this research topic, focusing on the recognition problem. Chaplick, T\"{o}pfer, Voborn\'{\i}k, and Zeman showed, for every fixed tree $T$, a polynomial-time algorithm recognizing $T$-graphs. Tucker showed a polynomial time algorithm recognizing $K_3$-graphs (circular-arc graphs). On the other hand, Chaplick at al. showed that recognition of $H$-graphs is $NP$-hard if $H$ contains two different cycles sharing an edge. The main two results of this work narrow the gap between the $NP$-hard and $P$ cases of $H$-graphs recognition. First, we show that recognition of $H$-graphs is $NP$-hard when $H$ contains two different cycles. On the other hand, we show a polynomial-time algorithm recognizing $L$-graphs, where $L$ is a graph containing a cycle and an edge attached to it ($L$-graphs are called lollipop graphs). Our work leaves open the recognition problems of $M$-graphs for every unicyclic graph $M$ different from a cycle and a lollipop. Other results of this work, which shed some light on the cases that remain open, are as follows. Firstly, the recognition of $M$-graphs, where $M$ is a fixed unicyclic graph, admits a polynomial time algorithm if we restrict the input to graphs containing particular holes (hence recognition of $M$-graphs is probably most difficult for chordal graphs). Secondly, the recognition of medusa graphs, which are defined as the union of $M$-graphs, where $M$ runs over all unicyclic graphs, is $NP$-complete.

We continue the study of $\delta$-dispersion, a continuous facility location problem on a graph where all edges have unit length and where the facilities may also be positioned in the interior of the edges. The goal is to position as many facilities as possible subject to the condition that every two facilities have distance at least $\delta$ from each other. Our main technical contribution is an efficient procedure to `round-up' distance $\delta$. It transforms a $\delta$-dispersed set $S$ into a $\delta^\star$-dispersed set $S^\star$ of same size where distance $\delta^\star$ is a slightly larger rational $\tfrac{a}{b}$ with a numerator $a$ upper bounded by the longest (not-induced) path in the input graph. Based on this rounding procedure and connections to the distance-$d$ independent set problem we derive a number of algorithmic results. When parameterized by treewidth, the problem is in XP. When parameterized by treedepth the problem is FPT and has a matching lower bound on its time complexity under ETH. Moreover, we can also settle the parameterized complexity with the solution size as parameter using our rounding technique: $\delta$-\dispersion is FPT for every $\delta \leq 2$ and W[1]-hard for every $\delta > 2$. Further, we show that $\delta$-dispersion is NP-complete for every fixed irrational distance $\delta$, which was left open in a previous work., Comment: 35 pages, accepted at MFCS 2022

A conflict-free coloring of a graph $G$ is a (partial) coloring of its vertices such that every vertex $u$ has a neighbor whose assigned color is unique in the neighborhood of $u$. There are two variants of this coloring, one defined using the open neighborhood and one using the closed neighborhood. For both variants, we study the problem of deciding whether the conflict-free coloring of a given graph $G$ is at most a given number $k$. In this work, we investigate the relation of clique-width and minimum number of colors needed (for both variants) and show that these parameters do not bound one another. Moreover, we consider specific graph classes, particularly graphs of bounded clique-width and types of intersection graphs, such as distance hereditary graphs, interval graphs and unit square and disk graphs. We also consider Kneser graphs and split graphs. We give (often tight) upper and lower bounds and determine the complexity of the decision problem on these graph classes, which improve some of the results from the literature. Particularly, we settle the number of colors needed for an interval graph to be conflict-free colored under the open neighborhood model, which was posed as an open problem., Comment: Accepted in Algorithmica

We investigate the parameterized complexity of the recognition problem for the proper $H$-graphs. The $H$-graphs are the intersection graphs of connected subgraphs of a subdivision of a multigraph $H$, and the properness means that the containment relationship between the representations of the vertices is forbidden. The class of $H$-graphs was introduced as a natural (parameterized) generalization of interval and circular-arc graphs by Bir\'o, Hujter, and Tuza in 1992, and the proper $H$-graphs were introduced by Chaplick et al. in WADS 2019 as a generalization of proper interval and circular-arc graphs. For these graph classes, $H$ may be seen as a structural parameter reflecting the distance of a graph to a (proper) interval graph, and as such gained attention as a structural parameter in the design of efficient algorithms. We show the following results. - For a tree $T$ with $t$ nodes, it can be decided in $ 2^{\mathcal{O}(t^2 \log t)} \cdot n^3 $ time, whether an $n$-vertex graph $ G $ is a proper $ T $-graph. For yes-instances, our algorithm outputs a proper $T$-representation. This proves that the recognition problem for proper $H$-graphs, where $H$ required to be a tree, is fixed-parameter tractable when parameterized by the size of $T$. Previously only NP-completeness was known. - Contrasting to the first result, we prove that if $H$ is not constrained to be a tree, then the recognition problem becomes much harder. Namely, we show that there is a multigraph $H$ with 4 vertices and 5 edges such that it is NP-complete to decide whether $G$ is a proper $H$-graph.

We investigate the so-called recoverable robust assignment problem on balanced bipartite graphs with $2n$ vertices, a mainstream problem in robust optimization: For two given linear cost functions $c_1$ and $c_2$ on the edges and a given integer $k$, the goal is to find two perfect matchings $M_1$ and $M_2$ that minimize the objective value $c_1(M_1)+c_2(M_2)$, subject to the constraint that $M_1$ and $M_2$ have at least $k$ edges in common. We derive a variety of results on this problem. First, we show that the problem is W[1]-hard with respect to the parameter $k$, and also with respect to the recoverability parameter $k'=n-k$. This hardness result holds even in the highly restricted special case where both cost functions $c_1$ and $c_2$ only take the values $0$ and $1$. (On the other hand, containment of the problem in XP is straightforward to see.) Next, as a positive result we construct a polynomial time algorithm for the special case where one cost function is Monge, whereas the other one is Anti-Monge. Finally, we study the variant where matching $M_1$ is frozen, and where the optimization goal is to compute the best corresponding matching $M_2$, the second stage recoverable assignment problem. We show that this problem variant is contained in the randomized parallel complexity class $\text{RNC}_2$, and that it is at least as hard as the infamous problem \probl{Exact Matching in Red-Blue Bipartite Graphs} whose computational complexity is a long-standing open problem

We introduce and study the complexity of Path Packing. Given a graph $G$ and a list of paths, the task is to embed the paths edge-disjoint in $G$. This generalizes the well known Hamiltonian-Path problem. Since Hamiltonian Path is efficiently solvable for graphs of small treewidth, we study how this result translates to the much more general Path Packing. On the positive side, we give an FPT-algorithm on trees for the number of paths as parameter. Further, we give an XP-algorithm with the combined parameters maximal degree, number of connected components and number of nodes of degree at least three. Surprisingly the latter is an almost tight result by runtime and parameterization. We show an ETH lower bound almost matching our runtime. Moreover, if two of the three values are constant and one is unbounded the problem becomes NP-hard. Further, we study restrictions to the given list of paths. On the positive side, we present an FPT-algorithm parameterized by the sum of the lengths of the paths. Packing paths of length two is polynomial time solvable, while packing paths of length three is NP-hard. Finally, even the spacial case EPC where the paths have to cover every edge in $G$ exactly once is already NP-hard for two paths on 4-regular graphs.

We study a continuous facility location problem on a graph where all edges have unit length and where the facilities may also be positioned in the interior of the edges. The goal is to position as many facilities as possible subject to the condition that any two facilities have at least distance $\delta$ from each other. We investigate the complexity of this problem in terms of the rational parameter $\delta$. The problem is polynomially solvable, if the numerator of $\delta$ is $1$ or $2$, while all other cases turn out to be NP-hard., Comment: 13 pages, 1 figure

The Target Set Selection problem takes as an input a graph $G$ and a non-negative integer threshold $ \mbox{thr}(v) $ for every vertex $v$. A vertex $v$ can get active as soon as at least $ \mbox{thr}(v) $ of its neighbors have been activated. The objective is to select a smallest possible initial set of vertices, the target set, whose activation eventually leads to the activation of all vertices in the graph. We show that Target Set Selection is in FPT when parameterized with the combined parameters clique-width of the graph and the maximum threshold value. This generalizes all previous FPT-membership results for the parameterization by maximum threshold, and thereby solves an open question from the literature. We stress that the time complexity of our algorithm is surprisingly well-behaved and grows only single-exponentially in the parameters.