Large independent sets via neighborhood block dynamics, Glauber dynamics, and local occupancy

Local occupancy can be used to bound the expected size of a hard-core independent set. The framework of locally stationary distributions shows that for triangle-free graphs, despite slow mixing at the relevant fugacity parameter, standard Glauber dynamics can be used to find sets close to the local occupancy guarantee. We improve the application of this framework to the hard-core model by replacing a conditional mixing argument with a more direct approach based on a short sequence of single-site updates that, from every initial configuration, achieves the required occupancy inequality. In general graphs, naive conditional mixing estimates or certain problematic structures in the neighborhood can in principle incur running time cost exponential in the maximum degree $Δ$. We design short update sequences that, via our relaxed criterion, yield running times polynomial in $Δ$ for graphs with a hereditary bound $b$ on the density of $K_{t,t}$ subgraphs in neighborhoods. Our methods handle the case where $t$ and $b$ can grow with the maximum degree, and the resulting algorithm attains the random graph shattering threshold when they grow sufficiently slowly. That is, we give improved local occupancy parameters and a short update sequence analysis which together show that Glauber dynamics finds independent sets of size $(1-o(1))n(\logΔ)/Δ$ in time polynomial in $n$ and $Δ$ whenever $t$ and $b$ are constant or grow suitably slowly. In a similar regime, we also establish that the (correspondence) chromatic number is at most $(1+o(1))Δ/\logΔ$ which improves upon prior results by a factor of at least $4$ in the case $t\ge 4$.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-11-02
DOI
https://doi.org/10.48550/arxiv.2611.02483
Primary Topic
Advanced Graph Theory Research
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Large independent sets via neighborhood block dynamics, Glauber dynamics, and local occupancy

Ewan Davies, Brian Tan
arXiv (Cornell University)
Advanced Graph Theory Research
preprint

Large independent sets via neighborhood block dynamics, Glauber dynamics, and local occupancy

Ewan Davies, Brian Tan
preprint en

Abstract

Local occupancy can be used to bound the expected size of a hard-core independent set. The framework of locally stationary distributions shows that for triangle-free graphs, despite slow mixing at the relevant fugacity parameter, standard Glauber dynamics can be used to find sets close to the local occupancy guarantee. We improve the application of this framework to the hard-core model by replacing a conditional mixing argument with a more direct approach based on a short sequence of single-site updates that, from every initial configuration, achieves the required occupancy inequality. In general graphs, naive conditional mixing estimates or certain problematic structures in the neighborhood can in principle incur running time cost exponential in the maximum degree $Δ$. We design short update sequences that, via our relaxed criterion, yield running times polynomial in $Δ$ for graphs with a hereditary bound $b$ on the density of $K_{t,t}$ subgraphs in neighborhoods. Our methods handle the case where $t$ and $b$ can grow with the maximum degree, and the resulting algorithm attains the random graph shattering threshold when they grow sufficiently slowly. That is, we give improved local occupancy parameters and a short update sequence analysis which together show that Glauber dynamics finds independent sets of size $(1-o(1))n(\logΔ)/Δ$ in time polynomial in $n$ and $Δ$ whenever $t$ and $b$ are constant or grow suitably slowly. In a similar regime, we also establish that the (correspondence) chromatic number is at most $(1+o(1))Δ/\logΔ$ which improves upon prior results by a factor of at least $4$ in the case $t\ge 4$.

arXiv (Cornell University)
Advanced Graph Theory Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon