Clustering to Given Connectivities - Archive ouverte HAL
Communication Dans Un Congrès Année : 2019

Clustering to Given Connectivities

Résumé

We define a general variant of the graph clustering problem where the criterion of density for the clusters is (high) connectivity. In \probCGClong, we are given an $n$-vertex graph $G$, an integer $k$, and a sequence $\Lambda=\langle \lambda_{1},\ldots,\lambda_{t}\rangle$ of positive integers and we ask whether it is possible to remove at most $k$ edges from $G$ such that the resulting connected components are {\sl exactly} $t$ and their corresponding edge connectivities are lower-bounded by the numbers in $\Lambda$. We prove that this problem, parameterized by $k$, is fixed parameter tractable i.e., can be solved by an $f(k)\cdot n^{O(1)}$-step algorithm, for some function $f$ that depends only on the parameter $k$. Our algorithm uses the recursive understanding technique that is especially adapted so to deal with the fact that we do not impose any restriction to the connectivity demands in $\Lambda$.
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hal-03003256 , version 1 (20-11-2020)

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Petr A. Golovach, Dimitrios M. Thilikos. Clustering to Given Connectivities. IPEC 2019 - 14th International Symposium on Parameterized and Exact Computation, Sep 2019, Munich, Germany. pp.18:1-18:17, ⟨10.4230/LIPIcs.IPEC.2019.18⟩. ⟨hal-03003256⟩
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