Linear Recognition of Almost Interval Graphs
Abstract
Let , , and denote the classes of graphs that can be obtained from some interval graph by adding vertices, adding edges, and deleting edges, respectively. When is small, these graph classes are called almost interval graphs. They are well motivated from computational biology, where the data ought to be represented by an interval graph while we can only expect an almost interval graph for the best. For any fixed , we give linear-time algorithms for recognizing all these classes, and in the case of membership, our algorithms provide also a specific interval graph as evidence. When is part of the input, these problems are also known as graph modification problems, all NP-complete. Our results imply that they are fixed-parameter tractable parameterized by , thereby resolving the long-standing open problem on the parameterized complexity of recognizing , first asked by Bodlaender et al. [Bioinformatics, 11:49--57, 1995]. Moreover, our algorithms for recognizing and run in times and , (where and stand for the numbers of vertices and edges respectively in the input graph,) significantly improving the -time algorithm of Heggernes et al. [STOC 2007] and the -time algorithm of Cao and Marx [SODA 2014] respectively.
Keywords
Cite
@article{arxiv.1403.1515,
title = {Linear Recognition of Almost Interval Graphs},
author = {Yixin Cao},
journal= {arXiv preprint arXiv:1403.1515},
year = {2014}
}
Comments
Completely restructured, and results on unit interval graphs have been dropped to make this version more focused