ﻻ يوجد ملخص باللغة العربية
By a well known result the treewidth of k-outerplanar graphs is at most 3k-1. This paper gives, besides a rigorous proof of this fact, an algorithmic implementation of the proof, i.e. it is shown that, given a k-outerplanar graph G, a tree decomposition of G of width at most 3k-1 can be found in O(kn) time and space. Similarly, a branch decomposition of a k-outerplanar graph of width at most 2k+1 can be also obtained in O(kn) time, the algorithm for which is also analyzed.
A natural way of increasing our understanding of NP-complete graph problems is to restrict the input to a special graph class. Classes of $H$-free graphs, that is, graphs that do not contain some graph $H$ as an induced subgraph, have proven to be an
We initiate the study of a new parameterization of graph problems. In a multiple interval representation of a graph, each vertex is associated to at least one interval of the real line, with an edge between two vertices if and only if an interval ass
k-means is a widely used clustering algorithm, but for $k$ clusters and a dataset size of $N$, each iteration of Lloyds algorithm costs $O(kN)$ time. Although there are existing techniques to accelerate single Lloyd iterations, none of these are tail
A bipartite graph $G=(A,B,E)$ is ${cal H}$-convex, for some family of graphs ${cal H}$, if there exists a graph $Hin {cal H}$ with $V(H)=A$ such that the set of neighbours in $A$ of each $bin B$ induces a connected subgraph of $H$. Many $mathsf{NP}$-
Computing cohesive subgraphs is a central problem in graph theory. While many formulations of cohesive subgraphs lead to NP-hard problems, finding a densest subgraph can be done in polynomial time. As such, the densest subgraph model has emerged as t