A 2k2k-Vertex Kernel for Maximum Internal Spanning Tree

We consider the parameterized version of the maximum internal spanning tree problem, which, given an $n$-vertex graph and a parameter $k$, asks for a spanning tree with at least $k$ internal vertices. Fomin et al. [J. Comput. System Sci., 79:1-6] crafted a very ingenious reduction rule, and showed that a simple application of this rule is sufficient to yield a $3k$-vertex kernel. Here we propose a novel way to use the same reduction rule, resulting in an improved $2k$-vertex kernel. Our algorithm applies first a greedy procedure consisting of a sequence of local exchange operations, which ends with a local-optimal spanning tree, and then uses this special tree to find a reducible structure. As a corollary of our kernel, we obtain a deterministic algorithm for the problem running in time $4^k \cdot n^{O(1)}$

Clique-width: When Hard Does Not Mean Impossible

In recent years, the parameterized complexity approach has lead to the introduction of many new algorithms and frameworks on graphs and digraphs of bounded clique-width and, equivalently, rank-width. However, despite intensive work on the subject, there still exist well-established hard problems where neither a parameterized algorithm nor a theoretical obstacle to its existence are known. Our article is interested mainly in the digraph case, targeting the well-known Minimum Leaf Out-Branching (cf. also Minimum Leaf Spanning Tree) and Edge Disjoint Paths problems on digraphs of bounded clique-width with non-standard new approaches. The first part of the article deals with the Minimum Leaf Out-Branching problem and introduces a novel XP-time algorithm wrt. clique-width. We remark that this problem is known to be W[2]-hard, and that our algorithm does not resemble any of the previously published attempts solving special cases of it such as the Hamiltonian Path. The second part then looks at the Edge Disjoint Paths problem (both on graphs and digraphs) from a different perspective -- rather surprisingly showing that this problem has a definition in the MSO_1 logic of graphs. The linear-time FPT algorithm wrt. clique-width then follows as a direct consequence

Revisiting Pattern Structures for Structured Attribute Sets

International audienceIn this paper, we revisit an original proposition on pattern structures for structured sets of attributes. There are several reasons for carrying out this kind of research work. The original proposition does not give many details on the whole framework, and especially on the possible ways of implementing the similarity operation. There exists an alternative definition without any reference to pattern structures, and we would like to make a parallel between two points of view. Moreover we discuss an efficient implementation of the intersection operation in the corresponding pattern structure. Finally, we discovered that pattern structures for structured attribute sets are very well adapted to the classification and the analysis of RDF data. We terminate the paper by an experimental section where it is shown that the provided implementation of pattern structures for structured attribute sets is quite efficient

Bounds on distances for spanning trees of graphs.

Master of Science in Applied Mathematics, University of KwaZulu-Natal, Westville, 2018.In graph theory, there are several techniques known in literature for constructing spanning trees. Some of these techniques yield spanning trees with many leaves. We will use these constructed spanning trees to bound several distance parameters. The cardinality of the vertex set of graph G is called the order, n(G) or n. The cardinality of the edge set of graph G is called the size, m(G) or m. The minimum degree of G, (G) or , is the minimum degree among the degrees of the vertices of G: A spanning tree T of a graph G is a subgraph that is a tree which includes all the vertices of G. The distance d(u; v) between two vertices u and v is the length of a shortest u-v path of G. The eccentricity, ec (v), of a vertex v 2 V (G) is the maximum distance from it to any other vertex in G. The diameter, diam(G) or d, is the maximum eccentricity amongst all vertices of G. The radius, rad(G), is the minimum eccentricity among all vertices of G. The average distance of a graph G, (G), is the expected distance between a randomly chosen pair of distinct vertices. We investigate how each constructed spanning tree can be used to bound diam- eter, radius or average distance in terms of order, size and minimum degree. The techniques to be considered include the radius-preserving spanning trees by Erd}os et al, the Ding et al technique, and the Dankelmann and Entringer technique. Finally, we use the Kleitman and West dead leaves technique to construct spanning trees with many leaves for various values of the minimum degree k (for k = 3; 4 and k > 4) and order n. We then use the leaf number to bound diameter.Date (2018) taken as per title page