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We derive upper and lower bounds on the degree $d$ for which the Lovasz $vartheta$ function, or equivalently sum-of-squares proofs with degree two, can refute the existence of a $k$-coloring in random regular graphs $G_{n,d}$. We show that this type of refutation fails well above the $k$-colorability transition, and in particular everywhere below the Kesten-Stigum threshold. This is consistent with the conjecture that refuting $k$-colorability, or distinguishing $G_{n,d}$ from the planted coloring model, is hard in this region. Our results also apply to the disassortative case of the stochastic block model, adding evidence to the conjecture that there is a regime where community detection is computationally hard even though it is information-theoretically possible. Using orthogonal polynomials, we also provide explicit upper bounds on $vartheta(overline{G})$ for regular graphs of a given girth, which may be of independent interest.
We prove that in sparse ErdH{o}s-R{e}nyi graphs of average degree $d$, the vector chromatic number (the relaxation of chromatic number coming from the Lov`{a}sz theta function) is typically $tfrac{1}{2}sqrt{d} + o_d(1)$. This fits with a long-standin
Spectral techniques have proved amongst the most effective approaches to graph clustering. However, in general they require explicit computation of the main eigenvectors of a suitable matrix (usually the Laplacian matrix of the graph). Recent work (e
Consider the normalized adjacency matrices of random $d$-regular graphs on $N$ vertices with fixed degree $dgeq3$. We prove that, with probability $1-N^{-1+{varepsilon}}$ for any ${varepsilon} >0$, the following two properties hold as $N to infty$ pr
This paper studies the problem of detecting the presence of a small dense community planted in a large ErdH{o}s-Renyi random graph $mathcal{G}(N,q)$, where the edge probability within the community exceeds $q$ by a constant factor. Assuming the hardn
Embedding a network in hyperbolic space can reveal interesting features for the network structure, especially in terms of self-similar characteristics. The hidden metric space, which can be thought of as the underlying structure of the network, is ab