Tur\'{a}n's Theorem Through Algorithmic Lens

TL;DR

This paper connects Extremal Graph Theory with algorithms by providing linear-time compression techniques to efficiently find large cliques and independent sets in graphs near Turán's bound, with tight complexity bounds.

Contribution

It introduces a linear-time compression algorithm linking Turán's theorem to clique detection, leading to new fixed-parameter algorithms for clique and independent set problems.

Findings

Linear-time reduction of clique problems to small graphs

Algorithms with exponential dependence on parameter k

Complexity bounds are tight under ETH

Abstract

The fundamental theorem of Tur\'{a}n from Extremal Graph Theory determines the exact bound on the number of edges $t_r(n)$ in an $n$-vertex graph that does not contain a clique of size $r+1$. We establish an interesting link between Extremal Graph Theory and Algorithms by providing a simple compression algorithm that in linear time reduces the problem of finding a clique of size $\ell$ in an $n$-vertex graph $G$ with $m \ge t_r(n)-k$ edges, where $\ell\leq r+1$, to the problem of finding a maximum clique in a graph on at most $5k$ vertices. This also gives us an algorithm deciding in time $2.49^{k}\cdot(n + m)$ whether $G$ has a clique of size $\ell$. As a byproduct of the new compression algorithm, we give an algorithm that in time $2^{\mathcal{O}(td^2)} \cdot n^2$ decides whether a graph contains an independent set of size at least $n/(d+1) + t$. Here $d$ is the average vertex degree of the graph $G$. The multivariate complexity analysis based on ETH indicates that the asymptotical dependence on several parameters in the running times of our algorithms is tight.