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Algorithmica
Article . 2023 . Peer-reviewed
License: CC BY
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https://doi.org/10.1007/978-3-...
Part of book or chapter of book . 2022 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2021
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Computing and Listing Avoidable Vertices and Paths

Authors: Charis Papadopoulos; Athanasios E. Zisis;

Computing and Listing Avoidable Vertices and Paths

Abstract

AbstractA simplicial vertex of a graph is a vertex whose neighborhood is a clique. It is known that listing all simplicial vertices can be done in O(nm) time or $$O(n^{\omega })$$ O ( n ω ) time, where $$O(n^{\omega })$$ O ( n ω ) is the time needed to perform a fast matrix multiplication. The notion of avoidable vertices generalizes the concept of simplicial vertices in the following way: a vertex u is avoidable if every induced path on three vertices with middle vertex u is contained in an induced cycle. We present algorithms for listing all avoidable vertices of a graph through the notion of minimal triangulations and common neighborhood detection. In particular we give algorithms with running times $$O(n^{2}m)$$ O ( n 2 m ) and $$O(n^{1+\omega })$$ O ( n 1 + ω ) , respectively. Additionally, based on a simplified graph traversal we propose a fast algorithm that runs in time $$O(n^2 + m^2)$$ O ( n 2 + m 2 ) and matches the corresponding running time of listing all simplicial vertices on sparse graphs with $$m=O(n)$$ m = O ( n ) . Moreover, we show that our algorithms cannot be improved significantly, as we prove that under plausible complexity assumptions there is no truly subquadratic algorithm for recognizing an avoidable vertex. To complement our results, we consider their natural generalizations of avoidable edges and avoidable paths. We propose an O(nm)-time algorithm that recognizes whether a given induced path is avoidable.

Keywords

FOS: Computer and information sciences, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS)

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
hybrid