Improved Hardness of Approximating k-Clique under ETH

Abstract: In this paper, we prove that assuming the exponential time hypothesis (ETH), there is no $$f(k)\cdot n^{{k^{{o(1/\log\log}} k)}}$$-time algorithm that can decide whether an $n$-vertex graph contains a clique of size $k$ or contains no clique of size $k/2$, and no FPT algorithm can decide whether an input graph has a clique of size $k$ or no clique of size $k/f(k)$, where $f(k)$ is some function in $k<sup>{1-o(1)}$. Our results significantly improve the previous works [Lin21, LRSW22]. The crux of our proof is a framework to construct gap-producing reductions for the $k$-Clique problem. More precisely, we show that given an error-correcting code $C:\Sigma_1<sup>k\to\Sigma_2<sup>{k&#39;}$ that is locally testable and smooth locally decodable in the parallel setting, one can construct a reduction which on input a graph $G$ outputs a graph $G&#39;$ in $(k&#39;)^{O(1)}\cdot n<sup>{O(\log|\Sigma_2|/\log|\Sigma_1|)}$ time such that: $\bullet$ If $G$ has a clique of size $k$, then $G&#39;$ has a clique of size $K$, where $K = (k&#39;)<sup>{O(1)}$. $\bullet$ If $G$ has no clique of size $k$, then $G&#39;$ has no clique of size $(1-\varepsilon)\cdot K$ for some constant $\varepsilon\in(0,1)$. We then construct such a code with $k&#39;=k^{{\Theta(\log\log} k)}$ and $|\Sigma_2|=|\Sigma_1|<sup>{k<sup>{0.54}}$, establishing the hardness results above. Our code generalizes the derivative code [WY07] into the case with a super constant order of derivatives.