In this paper, we study the Planted Clique problem in a semi-random model. Our model is inspired from the Feige-Kilian model [FK01] which has been studied in many other works [FK00, Ste17, MMT20] for a variety of graph problems. Our algorithm and analysis is on similar lines to the one studied for the Densest $k$-subgraph problem in the recent work of Khanna and Louis [KL20]. However since our algorithm fully recovers the planted clique w.h.p. (for a "large" range of input parameters), we require some new ideas. As a by-product of our main result, we give an alternate SDP based rounding algorithm (with matching guarantees) for solving the Planted Clique problem in a random graph. Also, we are able to solve special cases of the models introduced for the Densest $k$-subgraph problem in [KL20], when the planted subgraph is a clique instead of an arbitrary $d$-regular graph.
翻译:在本文中,我们在半随机模型中研究“原始克隆”问题。我们的模型来自Feige-Kilian模型[FK01],该模型已在许多其他工程[FK00、Ste17、MMT20]中研究过,用于各种图形问题。我们的算法和分析与Khanna和Louis最近工作中为“Densest $k$-subgraph”问题所研究的类似。然而,由于我们的算法完全恢复了所种植的 clique w.h.p.(输入参数的“大”范围),我们需要一些新想法。作为我们主要结果的副产品,我们提供了一种基于SDP的替代圆形算法(配对担保),以随机图解决原型克隆问题。此外,当种植的子谱不是任意的美元正统图时,我们能够解决在[KL20]中为“Densest $k$-subgraphy问题所引入的模型的特殊案例。