K-Clique: Difference between revisions

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== Parameters ==  
== Parameters ==  


n: number of vertices
$n$: number of vertices


k: size of clique
$m$: number of edges
 
$k$: size of clique


== Table of Algorithms ==  
== Table of Algorithms ==  

Latest revision as of 09:22, 10 April 2023

Description

For a constant $k \geq 3$, the $k$-Clique problem is as follows: given a graph $G = (V, E)$ on $n$ vertices, does $G$ contain $k$ distinct vertices $a_1, \ldots, a_k$ so that for every $(i, j)$, $i \neq j$, $(a_i, a_j ) \in E$? Such a $k$ node graph is called a $k$-clique.

Related Problems

Subproblem: Exact k-Clique, Min-Weight k-Clique, Max-Weight k-Clique

Related: Enumerating Maximal Cliques, arbitrary graph, Min-Weight k-Clique, Max-Weight k-Clique

Parameters

$n$: number of vertices

$m$: number of edges

$k$: size of clique

Table of Algorithms

Currently no algorithms in our database for the given problem.

Reductions TO Problem

Problem Implication Year Citation Reduction
CFG Recognition assume: k-Clique Hypothesis
then: there is no $O(N^{\omega-\epsilon}) time algorithm for target for any $\epsilon > {0}$
2017 https://ieeexplore.ieee.org/abstract/document/8104058 link
RNA Folding assume: k-Clique Hypothesis
then: there is no $O(N^{\omega-\epsilon}) time algorithm for target for any $\epsilon > {0}$
2017 https://ieeexplore.ieee.org/abstract/document/8104058 link
CFG Recognition assume: k-Clique Hypothesis
then: there is no $O(N^{\{3}-\epsilon}) time combinatorial algorithm for target for any $\epsilon > {0}$
2017 https://ieeexplore.ieee.org/abstract/document/8104058 link
RNA Folding assume: k-Clique Hypothesis
then: there is no $O(N^{\{3}-\epsilon}) time combinatorial algorithm for target for any $\epsilon > {0}$
2017 https://ieeexplore.ieee.org/abstract/document/8104058 link

References/Citation

https://dml.cz/bitstream/handle/10338.dmlcz/106381/CommentatMathUnivCarol_026-1985-2_22.pdf