Deterministic Distributed Ruling Sets of Line Graphs
May 18, 2018 · Declared Dead · 🏛 Colloquium on Structural Information & Communication Complexity
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Fabian Kuhn, Yannic Maus, Simon Weidner
arXiv ID
1805.07209
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.DC
Citations
25
Venue
Colloquium on Structural Information & Communication Complexity
Last Checked
3 months ago
Abstract
An $(α,β)$-ruling set of a graph $G=(V,E)$ is a set $R\subseteq V$ such that for any node $v\in V$ there is a node $u\in R$ in distance at most $β$ from $v$ and such that any two nodes in $R$ are at distance at least $α$ from each other. The concept of ruling sets can naturally be extended to edges, i.e., a subset $F\subseteq E$ is an $(α,β)$-ruling edge set of a graph $G=(V,E)$ if the corresponding nodes form an $(α,β)$-ruling set in the line graph of $G$. This paper presents a simple deterministic, distributed algorithm, in the $\mathsf{CONGEST}$ model, for computing $(2,2)$-ruling edge sets in $O(\log^* n)$ rounds. Furthermore, we extend the algorithm to compute ruling sets of graphs with bounded diversity. Roughly speaking, the diversity of a graph is the maximum number of maximal cliques a vertex belongs to. We devise $(2,O(\mathcal{D}))$-ruling sets on graphs with diversity $\mathcal{D}$ in $O(\mathcal{D}+\log^* n)$ rounds. This also implies a fast, deterministic $(2,O(\ell))$-ruling edge set algorithm for hypergraphs with rank at most $\ell$. Furthermore, we provide a ruling set algorithm for general graphs that for any $B\geq 2$ computes an $\big(α, α\lceil \log_B n \rceil \big)$-ruling set in $O(α\cdot B \cdot \log_B n)$ rounds in the $\mathsf{CONGEST}$ model. The algorithm can be modified to compute a $\big(2, β\big)$-ruling set in $O(βΔ^{2/β} + \log^* n)$ rounds in the $\mathsf{CONGEST}$~ model, which matches the currently best known such algorithm in the more general $\mathsf{LOCAL}$ model.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
📜 Similar Papers
In the same crypt — Data Structures & Algorithms
📚
📚
The Cartographer
R.I.P.
👻
Ghosted
Route Planning in Transportation Networks
R.I.P.
👻
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
👻
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
👻
Ghosted
Graph Isomorphism in Quasipolynomial Time
📚
📚
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way — 👻 Ghosted
R.I.P.
👻
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
👻
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
👻
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
👻
Ghosted