1,049 research outputs found

    Optimizing the trade-off between number of cops and capture time in Cops and Robbers

    Get PDF
    The cop throttling number thc(G)th_c(G) of a graph GG for the game of Cops and Robbers is the minimum of k+captk(G)k + capt_k(G), where kk is the number of cops and captk(G)capt_k(G) is the minimum number of rounds needed for kk cops to capture the robber on GG over all possible games in which both players play optimally. In this paper, we construct a family of graphs having thc(G)=Ω(n2/3)th_c(G)= \Omega(n^{2/3}), establish a sublinear upper bound on the cop throttling number, and show that the cop throttling number of chordal graphs is O(n)O(\sqrt{n}). We also introduce the product cop throttling number thc×(G)th_c^{\times}(G) as a parameter that minimizes the person-hours used by the cops. This parameter extends the notion of speed-up that has been studied in the context of parallel processing and network decontamination. We establish bounds on the product cop throttling number in terms of the cop throttling number, characterize graphs with low product cop throttling number, and show that for a chordal graph GG, thc×=1+rad(G)th_c^{\times}=1+rad(G).Comment: 19 pages, 3 figure

    Graph classes and forbidden patterns on three vertices

    Full text link
    This paper deals with graph classes characterization and recognition. A popular way to characterize a graph class is to list a minimal set of forbidden induced subgraphs. Unfortunately this strategy usually does not lead to an efficient recognition algorithm. On the other hand, many graph classes can be efficiently recognized by techniques based on some interesting orderings of the nodes, such as the ones given by traversals. We study specifically graph classes that have an ordering avoiding some ordered structures. More precisely, we consider what we call patterns on three nodes, and the recognition complexity of the associated classes. In this domain, there are two key previous works. Damashke started the study of the classes defined by forbidden patterns, a set that contains interval, chordal and bipartite graphs among others. On the algorithmic side, Hell, Mohar and Rafiey proved that any class defined by a set of forbidden patterns can be recognized in polynomial time. We improve on these two works, by characterizing systematically all the classes defined sets of forbidden patterns (on three nodes), and proving that among the 23 different classes (up to complementation) that we find, 21 can actually be recognized in linear time. Beyond this result, we consider that this type of characterization is very useful, leads to a rich structure of classes, and generates a lot of open questions worth investigating.Comment: Third version version. 38 page
    • …
    corecore