Abstract For a set of graphs $${\mathcal {H}}$$ H , a graph G is $${\mathcal {H}}$$ H -subgraph-free if G does not contain any graph from $${{{\mathcal {H}}}}$$ H as a subgraph. We propose general and easy-to-state conditions on graph problems that explain a large set of results for $${\mathcal {H}}$$ H -subgraph-free graphs. Namely, a graph problem must be efficiently solvable on graphs of bounded treewidth, computationally hard on subcubic graphs, and computational hardness must be preserved under edge subdivision of subcubic graphs. Our meta-classification says that if a graph problem $$\Pi $$ Π satisfies all three conditions, then for every finite set $${{{\mathcal {H}}}}$$ H , it is “efficiently solvable” on $${{{\mathcal {H}}}}$$ H -subgraph-free graphs if $${\mathcal {H}}$$ H contains a disjoint union of one or more paths and subdivided claws, and $$\Pi $$ Π is “computationally hard” otherwise. We apply our meta-classification on many well-known partitioning, covering and packing problems, network design problems and width parameter problems to obtain a dichotomy between polynomial-time solvability and -completeness. For distance-metric problems, we obtain a dichotomy between almost-linear-time solvability and having no subquadratic-time algorithm (conditioned on some hardness hypotheses). Apart from capturing a large number of explicitly and implicitly known results in the literature, we also prove a number of new results. Moreover, we perform an extensive comparison between the subgraph framework and the existing frameworks for the minor and topological minor relations, and pose several new open problems and research directions.

We study Steiner Forest on H -subgraph-free graphs, that is, graphs that do not contain some fixed graph H as a (not necessarily induced) subgraph. In contrast to the related Steiner Tree problem, Steiner Forest falls outside a recent framework that completely characterizes the complexity of many problems on H -subgraph-free graphs. Hence, the complexity of Steiner Forest on H -subgraph-free graphs remained open. Our main results are four polynomial-time algorithms for different excluded graphs H that are central to further understand its complexity. We also study the complexity of Steiner Forest for graphs with a small c -deletion set, that is, a small set X of vertices such that each connected component of G − X has size at most c . For this parameter, we give two algorithms that we later employ as subroutines (including a faster algorithm when c = 1 , that is, the vertex cover number) and exhibit a dichotomy theorem.

Abstract. The intensively studied Diameter problem is to find the diameter of a given connected graph. We investigate, for the first time in a structured manner, the complexity of Diameter for [Formula: see text]-free graphs, that is, graphs that do not contain a fixed graph [Formula: see text] as an induced subgraph. We first show that if [Formula: see text] is not a linear forest with small components, then Diameter cannot be solved in subquadratic time for [Formula: see text]-free graphs under SETH. For some small linear forests, we do show linear-time algorithms for solving Diameter. For other linear forests [Formula: see text], we make progress towards linear-time algorithms by considering specific diameter values. If [Formula: see text] is a linear forest, the maximum value of the diameter of any graph in a connected [Formula: see text]-free graph class is some constant [Formula: see text] dependent only on [Formula: see text]. We give linear-time algorithms for deciding if a connected [Formula: see text]-free graph has diameter [Formula: see text] for several linear forests [Formula: see text]. In contrast, for one such linear forest [Formula: see text], Diameter cannot be solved in subquadratic time for [Formula: see text]-free graphs under SETH. Moreover, we even show that, for several other linear forests [Formula: see text], one cannot decide in subquadratic time if a connected [Formula: see text]-free graph has diameter [Formula: see text] under SETH.

We consider a natural generalization of Vertex Cover : the Subset Vertex Cover problem, which is to decide for a graph G = ( V , E ) , a subset T ⊆ V and integer k , if V has a subset S of size at most k , such that S contains at least one end-vertex of every edge incident to a vertex of T . A graph is H -free if it does not contain H as an induced subgraph. We solve two open problems from the literature by proving that Subset Vertex Cover is NP -complete on subcubic (claw, diamond)-free planar graphs and on 2-unipolar graphs, a subclass of 2 P 3 -free weakly chordal graphs. Our results show for the first time that Subset Vertex Cover is computationally harder than Vertex Cover (under P ≠ NP ). We also prove new polynomial time results, some of which follow from a reduction to Vertex Cover restricted to classes of probe graphs. We first give a dichotomy on graphs where G [ T ] is H -free. Namely, we show that Subset Vertex Cover is polynomial-time solvable on graphs G , for which G [ T ] is H -free, if H = s P 1 + t P 2 and NP -complete otherwise. Moreover, we prove that Subset Vertex Cover is polynomial-time solvable for ( s P 1 + P 2 + P 3 ) -free graphs and bounded mim-width graphs. By combining our new results with known results we obtain a partial complexity classification for Subset Vertex Cover on H -free graphs.

Abstract We initiate the investigation of the parameterized complexity of Diameter and Connectivity in the streaming paradigm. On the positive end, we show that knowing a vertex cover of size k allows for algorithms in the Adjacency List (AL) streaming model whose number of passes is constant and memory is $$\mathcal {O}(\log n)$$ O ( log n ) for any fixed k. Underlying these algorithms is a method to execute a breadth-first search in $$\mathcal {O}(k)$$ O ( k ) passes and $$\mathcal {O}(k \log n)$$ O ( k log n ) bits of memory. On the negative end, we show that many other parameters lead to lower bounds in the AL model, where $$\Omega (n/p)$$ Ω ( n / p ) bits of memory is needed for any p-pass algorithm even for constant parameter values. In particular, this holds for graphs with a known modulator (deletion set) of constant size to a graph that has no induced subgraph isomorphic to a fixed graph H, for most H. For some cases, we can also show one-pass, $$\Omega (n \log n)$$ Ω ( n log n ) bits of memory lower bounds. We also prove a much stronger $$\Omega (n^2/p)$$ Ω ( n 2 / p ) lower bound for Diameter on bipartite graphs. Finally, using the insights we developed into streaming parameterized graph exploration algorithms, we show a new streaming kernelization algorithm for computing a vertex cover of size k. This yields a kernel of 2k vertices (with $$\mathcal {O}(k^2)$$ O ( k 2 ) edges) produced as a stream in $$\text {poly}(k)$$ poly ( k ) passes and only $$\mathcal {O}(k \log n)$$ O ( k log n ) bits of memory.

The Integer Multicommodity Flow problem has been studied extensively in the literature. However, from a parameterised perspective, mostly special cases, such as the Disjoint Path problem, have been considered. Therefore, we investigate the parameterised complexity of the general Integer Multicommodity Flow problem. We show that the decision version of this problem on directed graphs for a constant number of commodities, when the capacities are given in unary, is XNLP-complete with pathwidth as parameter and XALP-complete with treewidth as parameter. When the capacities are given in binary, the problem is NP-complete even for graphs of pathwidth at most 13. We give related results for undirected graphs. These results imply that the problem is unlikely to be fixed-parameter tractable by these parameters. In contrast, we show that the problem does become fixed-parameter tractable when weighted tree partition width (a variant of tree partition width for edge weighted graphs) is used as parameter.

Streaming is a model where an input graph is provided one edge at a time, instead of being able to inspect it at will. In this work, we take a parameterized approach by assuming a vertex cover of the graph is given, building on work of Bishnu et al. [COCOON 2020]. We show the further potency of combining this parameter with the Adjacency List streaming model to obtain results for vertex deletion problems. This includes kernels, parameterized algorithms, and lower bounds for the problems of Π-free Deletion, H-free Deletion, and the more specific forms of Cluster Vertex Deletion and Odd Cycle Transversal. We focus on the complexity in terms of the number of passes over the input stream, and the memory used. This leads to a pass/memory trade-off, where a different algorithm might be favourable depending on the context and instance. We also discuss implications for parameterized complexity in the non-streaming setting.

We initiate the investigation of the parameterized complexity of Diameter and Connectivity in the streaming paradigm. On the positive end, we show that knowing a vertex cover of size $k$ allows for algorithms in the Adjacency List (AL) streaming model whose number of passes is constant and memory is $O(\log n)$ for any fixed $k$. Underlying these algorithms is a method to execute a breadth-first search in $O(k)$ passes and $O(k \log n)$ bits of memory. On the negative end, we show that many other parameters lead to lower bounds in the AL model, where $Ω(n/p)$ bits of memory is needed for any $p$-pass algorithm even for constant parameter values. In particular, this holds for graphs with a known modulator (deletion set) of constant size to a graph that has no induced subgraph isomorphic to a fixed graph $H$, for most $H$. For some cases, we can also show one-pass, $Ω(n \log n)$ bits of memory lower bounds. We also prove a much stronger $Ω(n^2/p)$ lower bound for Diameter on bipartite graphs. Finally, using the insights we developed into streaming parameterized graph exploration algorithms, we show a new streaming kernelization algorithm for computing a vertex cover of size $k$. This yields a kernel of $2k$ vertices (with $O(k^2)$ edges) produced as a stream in $\text{poly}(k)$ passes and only $O(k \log n)$ bits of memory.