Optimal Distance Labeling for Permutation Graphs
July 16, 2024 Β· Declared Dead Β· π International Colloquium on Automata, Languages and Programming
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
PaweΕ Gawrychowski, Wojciech Janczewski
arXiv ID
2407.12147
Category
cs.DS: Data Structures & Algorithms
Citations
1
Venue
International Colloquium on Automata, Languages and Programming
Last Checked
4 months ago
Abstract
A permutation graph is the intersection graph of a set of segments between two parallel lines. In other words, they are defined by a permutation $Ο$ on $n$ elements, such that $u$ and $v$ are adjacent if an only if $u<v$ but $Ο(u)>Ο(v)$. We consider the problem of computing the distances in such a graph in the setting of informative labeling schemes. The goal of such a scheme is to assign a short bitstring $\ell(u)$ to every vertex $u$, such that the distance between $u$ and $v$ can be computed using only $\ell(u)$ and $\ell(v)$, and no further knowledge about the whole graph (other than that it is a permutation graph). This elegantly captures the intuition that we would like our data structure to be distributed, and often leads to interesting combinatorial challenges while trying to obtain lower and upper bounds that match up to the lower-order terms. For distance labeling of permutation graphs on $n$ vertices, Katz, Katz, and Peleg [STACS 2000] showed how to construct labels consisting of $\mathcal{O}(\log^{2} n)$ bits. Later, Bazzaro and Gavoille [Discret. Math. 309(11)] obtained an asymptotically optimal bounds by showing how to construct labels consisting of $9\log{n}+\mathcal{O}(1)$ bits, and proving that $3\log{n}-\mathcal{O}(\log{\log{n}})$ bits are necessary. This however leaves a quite large gap between the known lower and upper bounds. We close this gap by showing how to construct labels consisting of $3\log{n}+\mathcal{O}(\log\log n)$ bits.
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