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A Unified View of Graph Regularity via Matrix Decompositions

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arxiv 1911.11868 v5 pith:FR4KY5CP submitted 2019-11-26 cs.DS math.CO

classification cs.DSmath.CO
keywords graphsregularitylemmasclassclassesgraphmatrixprove
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abstract

We prove algorithmic weak and \Szemeredi{} regularity lemmas for several classes of sparse graphs in the literature, for which only weak regularity lemmas were previously known. These include core-dense graphs, low threshold rank graphs, and (a version of) $L^p$ upper regular graphs. More precisely, we define \emph{cut pseudorandom graphs}, we prove our regularity lemmas for these graphs, and then we show that cut pseudorandomness captures all of the above graph classes as special cases. The core of our approach is an abstracted matrix decomposition, roughly following Frieze and Kannan [Combinatorica '99] and \Lovasz{} and Szegedy [Geom.\ Func.\ Anal.\ '07], which can be computed by a simple algorithm by Charikar [AAC0 '00]. This gives rise to the class of cut pseudorandom graphs, and using work of Oveis Gharan and Trevisan [TOC '15], it also implies new PTASes for MAX-CUT, MAX-BISECTION, MIN-BISECTION for a significantly expanded class of input graphs. (It is NP Hard to get PTASes for these graphs in general.)

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  1. List Decoding Expander-Based Codes up to Capacity in Near-Linear Time

    cs.DS 2025-04 conditional novelty 8.0 of 10

    Near-linear-time list decoding and list recovery up to capacity are achieved for expander-based AEL and Tanner codes using a graph-regularity rigidity framework.

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