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APPROX

DOI: 10.4230/LIPIcs.APPROX/RANDOM.2024.5

Asynchronous Majority Dynamics on Binomial Random Graphs

Authors: Divyarthi Mohan and Paweł Prałat

Published in: LIPIcs, Volume 317, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)

Abstract

We study information aggregation in networks when agents interact to learn a binary state of the world. Initially each agent privately observes an independent signal which is correct with probability 1/2+δ for some δ > 0. At each round, a node is selected uniformly at random to update their public opinion to match the majority of their neighbours (breaking ties in favour of their initial private signal). Our main result shows that for sparse and connected binomial random graphs G(n,p) the process stabilizes in a correct consensus in 𝒪(nlog² n/log log n) steps with high probability. In fact, when log n/n ≪ p = o(1) the process terminates at time T^ = (1+o(1))nlog n, where T^ is the first time when all nodes have been selected at least once. However, in dense binomial random graphs with p = Ω(1), there is an information cascade where the process terminates in the incorrect consensus with probability bounded away from zero.

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Divyarthi Mohan and Paweł Prałat. Asynchronous Majority Dynamics on Binomial Random Graphs. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 5:1-5:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{mohan_et_al:LIPIcs.APPROX/RANDOM.2024.5,
  author =	{Mohan, Divyarthi and Pra{\l}at, Pawe{\l}},
  title =	{{Asynchronous Majority Dynamics on Binomial Random Graphs}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)},
  pages =	{5:1--5:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-348-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{317},
  editor =	{Kumar, Amit and Ron-Zewi, Noga},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2024.5},
  URN =		{urn:nbn:de:0030-drops-209985},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2024.5},
  annote =	{Keywords: Opinion dynamics, Social learning, Stochastic processes, Random Graphs, Consensus}
}

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RANDOM

DOI: 10.4230/LIPIcs.APPROX/RANDOM.2022.29

A Fully Adaptive Strategy for Hamiltonian Cycles in the Semi-Random Graph Process

Authors: Pu Gao, Calum MacRury, and Paweł Prałat

Published in: LIPIcs, Volume 245, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022)

Abstract

The semi-random graph process is a single player game in which the player is initially presented an empty graph on n vertices. In each round, a vertex u is presented to the player independently and uniformly at random. The player then adaptively selects a vertex v, and adds the edge uv to the graph. For a fixed monotone graph property, the objective of the player is to force the graph to satisfy this property with high probability in as few rounds as possible. We focus on the problem of constructing a Hamiltonian cycle in as few rounds as possible. In particular, we present an adaptive strategy for the player which achieves it in α n rounds, where α < 2.01678 is derived from the solution to some system of differential equations. We also show that the player cannot achieve the desired property in less than β n rounds, where β > 1.26575. These results improve the previously best known bounds and, as a result, the gap between the upper and lower bounds is decreased from 1.39162 to 0.75102.

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Pu Gao, Calum MacRury, and Paweł Prałat. A Fully Adaptive Strategy for Hamiltonian Cycles in the Semi-Random Graph Process. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 245, pp. 29:1-29:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{gao_et_al:LIPIcs.APPROX/RANDOM.2022.29,
  author =	{Gao, Pu and MacRury, Calum and Pra{\l}at, Pawe{\l}},
  title =	{{A Fully Adaptive Strategy for Hamiltonian Cycles in the Semi-Random Graph Process}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022)},
  pages =	{29:1--29:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-249-5},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{245},
  editor =	{Chakrabarti, Amit and Swamy, Chaitanya},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2022.29},
  URN =		{urn:nbn:de:0030-drops-171517},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2022.29},
  annote =	{Keywords: Random graphs and processes, Online adaptive algorithms, Hamiltonian cycles, Differential equation method}
}

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Documents authored by Prałat, Paweł

Asynchronous Majority Dynamics on Binomial Random Graphs

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A Fully Adaptive Strategy for Hamiltonian Cycles in the Semi-Random Graph Process

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