A linear time algorithm to compute the impact of all the articulation points
April 01, 2015 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Gabriele Farina
arXiv ID
1504.00341
Category
cs.DS: Data Structures & Algorithms
Citations
3
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The articulation points of an undirected connected graphs are those vertices whose removal increases the number of connected components of the graph, i.e. the vertices whose removal disconnects the graph. However, not all the articulation points are equal: the removal of some of them might end in a single vertex disconnected from the graph, whilst in other cases the graph can be split in several small pieces. In order to measure the effect of the removal of an articulation point, in \cite{AFL12} has been proposed the impact, defined as the number of vertices that get disconnected from the main (largest) surviving connected component (CC). In this paper we present the first linear time algorithm ($\mathcal{O}(m+n)$ for a graph with $n$ vertices and $m$ edges) to compute the impact of all the articulation points of the graph, thus improving from the $\mathcal{O}(a(m+n))\approx\mathcal{O}(nm+n^2)$ of the naΓ―ve algorithm, with $a$ being the number of articulation points of the graph.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted