The use of a pruned modular decomposition for Maximum Matching algorithms on some graph classes
Guillaume Ducoffe, Alexandru Popa

TL;DR
This paper introduces a pruned modular decomposition approach to efficiently solve Maximum Matching on certain graph classes, including distance-hereditary graphs, by leveraging structural properties and pruning rules.
Contribution
It presents a novel pruning-based framework that extends linear-time Maximum Matching algorithms to broader graph classes via pruned modular decomposition.
Findings
Linear-time algorithms for Maximum Matching on distance-hereditary graphs.
Maximum Matching can be computed efficiently on graphs with bounded pruned quotient subgraphs.
The framework generalizes previous methods and applies to larger graph classes.
Abstract
We address the following general question: given a graph class C on which we can solve Maximum Matching in (quasi) linear time, does the same hold true for the class of graphs that can be modularly decomposed into C ? A major difficulty in this task is that the Maximum Matching problem is not preserved by quotient, thereby making difficult to exploit the structural properties of the quotient subgraphs of the modular decomposition. So far, we are only aware of a recent framework in [Coudert et al., SODA'18] that only applies when the quotient subgraphs have bounded order and/or under additional assumptions on the nontriv-ial modules in the graph. As a first attempt toward improving this framework we study the combined effect of modular decomposition with a pruning process over the quotient subgraphs. More precisely, we remove sequentially from all such subgraphs their so-called…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplexity and Algorithms in Graphs · Advanced Graph Theory Research · Optimization and Search Problems
