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Tight Lower Bounds for Planted Clique in the Degree-4 SOS Program

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arxiv 1507.05136 v3 pith:4DNRRTGF submitted 2015-07-18 cs.DS cs.CC

classification cs.DScs.CC
keywords tildeomegacliquelowerbounddegree-4graphplanted
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abstract

We give a lower bound of $\tilde{\Omega}(\sqrt{n})$ for the degree-4 Sum-of-Squares SDP relaxation for the planted clique problem. Specifically, we show that on an Erd\"os-R\'enyi graph $G(n,\tfrac{1}{2})$, with high probability there is a feasible point for the degree-4 SOS relaxation of the clique problem with an objective value of $\tilde{\Omega}(\sqrt{n})$, so that the program cannot distinguish between a random graph and a random graph with a planted clique of size $\tilde{O}(\sqrt{n})$. This bound is tight. We build on the works of Deshpande and Montanari and Meka et al., who give lower bounds of $\tilde{\Omega}(n^{1/3})$ and $\tilde{\Omega}(n^{1/4})$ respectively. We improve on their results by making a perturbation to the SDP solution proposed in their work, then showing that this perturbation remains PSD as the objective value approaches $\tilde{\Omega}(n^{1/2})$. In an independent work, Hopkins, Kothari and Potechin [HKP15] have obtained a similar lower bound for the degree-$4$ SOS relaxation.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Average-Case Lower Bounds for Learning Sparse Mixtures, Robust Estimation and Semirandom Adversaries

    cs.CC 2019-08 accept novelty 8.0 of 10

    Assuming a k-partite planted clique conjecture, the authors prove tight k-to-k^2 sample-complexity lower bounds for robust sparse mean estimation, semirandom community recovery, and a universal class of sparse mixture...

  2. Fundamental Limits of Query-Based Subgraph Detection

    math.ST 2026-07 conditional novelty 7.0 of 10

    For non-adaptive edge-query detection of arbitrary planted subgraphs, the minimum query count is governed by whether the planted graph has dense local witnesses, high-degree hubs, or just many edges.

  3. Recovery of Planted Subgraphs

    cs.IT 2026-07 unverdicted novelty 6.0 of 10

    Sharp conditions for exact recovery of general planted subgraphs in ER graphs are given by the minimal maximum subgraph density, with matching bounds, a spectral algorithm, and computational hardness results via low-d...

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