Parameterized Algorithms for Colored Clustering

Abstract

In the Colored Clustering problem, one is asked to cluster edge-colored (hyper-)graphs whose colors represent interaction types. More specifically, the goal is to select as many edges as possible without choosing two edges that share an endpoint and are colored differently. Equivalently, the goal can also be described as assigning colors to the vertices in a way that fits the edge-coloring as well as possible. As this problem is NP-hard, we build on previous work by studying its parameterized complexity. We give a 2ᴼ⁽ᵏ⁾·nᴼ⁽¹⁾-time algorithm where k is the number of edges to be selected and n the number of vertices. We also prove the existence of a problem kernel of size O(k⁵ᐟ²), resolving an open problem posed in the literature. We consider parameters that are smaller than k, the number of edges to be selected, and r, the number of edges that can be deleted. Such smaller parameters are obtained by considering the difference between k or r and some lower bound on these values. We give both algorithms and lower bounds for Colored Clustering with such parameterizations. Finally, we settle the parameterized complexity of Colored Clustering with respect to structural graph parameters by showing that it is W[1]-hard with respect to both vertex cover number and tree-cut width, but fixed-parameter tractable with respect to local feedback edge number.

A review on current progress of semantic segmentation

“…After CAI Leizhen and LEUNG On Yin turned to the parameterized complexity of Coloured Clustering, and give FPT algorithms, where parameter k is for both the number of stable edges and the number of unstable edges [19]. In February 2023 Leon Kellerhals et al provided more detailed information about color clustering [20]. To extend the former study, they gave an algorithm, where k represents the number of edges to be selected and n corresponds to the number of vertices.…”