We study the problem of deleting the smallest set S of vertices (resp. edges) from a given graph G such that the induced subgraph (resp. subgraph) G \ S belongs to some class H. We consider the case where graphs in H have treewidth bounded by t, and give a general framework to obtain approximation algorithms for both vertex and edge-deletion settings from approximation algorithms for certain na...

Two approaches have been used to perform approximate inference in Bayesian networks for which exact inference is infeasible: employing an approximation algorithm, or approximating the structure. In this article we compare two structure-approximation techniques, edge-deletion and approximate structure learning based on sub-sampling, in terms of relative accuracy and computational efficiency. Our...

The line graph of a G has one node per each edge G, two them being adjacent only when the corresponding edges have in common. In this work, we consider problem finding minimum number to delete so that resulting is graph, which presents an interesting application haplotyping diploid organisms. We propose Integer Linear Programming formulation for problem. compare our approach with other existing...

Abstract In online edge- and node-deletion problems the input arrives node by an algorithm has to delete nodes or edges in order keep graph a given class $$\Pi $$ Π at all times. We consider only hereditary properties , for which optimal algorithms exist can be characterized set of forbidden subgraphs $${{\ma...

Edge deletion problems are those where given a graph G and a graph property π, the goal is to find a subset of edges such that after its removal the graph G will satisfy the property π. Typically, we want to minimize the number of edges removed. In fair deletion problem we change the objective: we minimize the maximum number of edges incident to a single vertex. We study the parameterized compl...