Fast Convergence to Unanimity in Dense Erdős-Rényi Graphs
October 12, 2022 · Declared Dead · 🏛 arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ran Tamir
arXiv ID
2210.05992
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.DC,
cs.DM,
math.PR
Citations
3
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Majority dynamics on the binomial Erdős-Rényi graph $\mathsf{G}(n,p)$ with $p=λ/\sqrt{n}$ is studied. In this process, each vertex has a state in $\{0,1\}$ and at each round, every vertex adopts the state of the majority of its neighbors, retaining its state in the case of a tie. It was conjectured by Benjamini et al. and proved by Fountoulakis et al. that this process reaches unanimity with high probability in at most four rounds. By adding some extra randomness and allowing the underlying graph to be drawn anew in each communication round, we improve on their result and prove that this process reaches consensus in only three communication rounds with probability approaching $1$ as $n$ grows to infinity. We also provide a converse result, showing that three rounds are not only sufficient, but also necessary.
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