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Graph structure via local occupancy

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arxiv 2003.14361 v1 pith:3EMKWUGN submitted 2020-03-31 math.CO

classification math.CO
keywords localboundedgraphgraphsstructurealonfirsthard-core
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The first author together with Jenssen, Perkins and Roberts (2017) recently showed how local properties of the hard-core model on triangle-free graphs guarantee the existence of large independent sets, of size matching the best-known asymptotics due to Shearer (1983). The present work strengthens this in two ways: first, by guaranteeing stronger graph structure in terms of colourings through applications of the Lov\'asz local lemma; and second, by extending beyond triangle-free graphs in terms of local sparsity, treating for example graphs of bounded local edge density, of bounded local Hall ratio, and of bounded clique number. This generalises and improves upon much other earlier work, including that of Shearer (1995), Alon (1996) and Alon, Krivelevich and Sudakov (1999), and more recent results of Molloy (2019), Bernshteyn (2019) and Achlioptas, Iliopoulos and Sinclair (2019). Our results derive from a common framework built around the hard-core model. It pivots on a property we call local occupancy, giving a clean separation between the methods for deriving graph structure with probabilistic information and verifying the requisite probabilistic information itself.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 11 citations worldwide. Full citation record

  1. Sharp bounds for the fractional chromatic number of high-girth $d$-degenerate graphs

    math.CO 2026-07 conditional novelty 7.0 of 10

    For d-degenerate C4-free graphs, the fractional chromatic number is at most (1+o(1))d/log d, and for every fixed girth at least 4 there are d-degenerate graphs with fractional chromatic number at least (1−o(1))d/log d.

  2. "I'm Not Able to Be There for You": Emotional Labour, Responsibility, and AI in Peer Support

    cs.HC 2026-04 unverdicted novelty 6.0 of 10

    Peer supporters bear concentrated emotional labor from institutional ambiguity and judge AI by its effects on redistributing responsibility and risk within fragile support roles.

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