Finding Hidden Cliques in Linear Time with High Probability

TL;DR

This paper introduces a new algorithm for detecting hidden cliques in random graphs that operates in quadratic time and has a very high probability of success, improving upon previous methods in efficiency and reliability.

Contribution

The paper presents a novel quadratic-time algorithm for finding hidden cliques with a failure probability that is polynomially small, enhancing previous approaches in speed and accuracy.

Findings

Algorithm runs in O(n^2) time.

Achieves success probability greater than polynomially small.

Improves reliability over previous spectral and simpler algorithms.

Abstract

We are given a graph $G$ with $n$ vertices, where a random subset of $k$ vertices has been made into a clique, and the remaining edges are chosen independently with probability $\tfrac12$. This random graph model is denoted $G(n,\tfrac12,k)$. The hidden clique problem is to design an algorithm that finds the $k$-clique in polynomial time with high probability. An algorithm due to Alon, Krivelevich and Sudakov uses spectral techniques to find the hidden clique with high probability when $k = c \sqrt{n}$ for a sufficiently large constant $c > 0$. Recently, an algorithm that solves the same problem was proposed by Feige and Ron. It has the advantages of being simpler and more intuitive, and of an improved running time of $O(n^2)$. However, the analysis in the paper gives success probability of only $2/3$. In this paper we present a new algorithm for finding hidden cliques that both runs in time $O(n^2)$, and has a failure probability that is less than polynomially small.