Algorithmic Decorrelation and Planted Clique in Dependent Random Graphs: The Case of Extra Triangles

We aim to understand the extent to which the noise distribution in a planted signal-plus-noise problem impacts its computational complexity. To that end, we consider the planted clique and planted dense subgraph problems, but in a different ambient graph. Instead of Erd\H{o}s-R\'enyi , which has independent edges, we take the ambient graph to be the \emph{random graph with triangles} (RGT) obtained by adding triangles to . We show that the RGT can be efficiently mapped to the corresponding , and moreover, that the planted clique (or dense subgraph) is approximately preserved under this mapping. This constitutes the first average-case reduction transforming dependent noise to independent noise. Together with the easier direction of mapping the ambient graph from Erd\H{o}s-R\'enyi to RGT, our results yield a strong equivalence between models. In order to prove our results, we develop a new general framework for reasoning about the validity of average-case reductions based on \emph{low sensitivity to perturbations}.

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