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20202026
most citedEfficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings

1 citations · 1 across the 9 of their papers we have counts for

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cs.DS2026

Parameterized Local Search for Vertex Cover: When only the Search Radius is Crucial

Christian Komusiewicz, Nils Morawietz

A vertex set in a graph is a valid -swap for a vertex cover of if has size at most and , the symmetric differenc…

cs.DS2026

Towards Settling the Complexity of the Lettericity Problem

Mario Grobler, Nils Morawietz, Silas Cato Sacher

The lettericity of a graph is defined as the smallest size of an alphabet such that there is a word and a decoder $\mathcal{D} \subseteq Σ…

cs.DS2025

Timeline Problems in Temporal Graphs: Vertex Cover vs. Dominating Set

Anton Herrmann, Christian Komusiewicz, Nils Morawietz +1

A temporal graph is a finite sequence of graphs, called snapshots, over the same vertex set. Many temporal graph problems turn out to be much more difficult than their static count…

cs.DS2025

Fantastic Flips and Where to Find Them: A General Framework for Parameterized Local Search on Partitioning Problems

Niels Grüttemeier, Nils Morawietz, Frank Sommer

Parameterized local search combines classic local search heuristics with the paradigm of parameterized algorithmics. While most local search algorithms aim to improve given solutio…

cs.DS2024

On the Complexity of Community-aware Network Sparsification

Emanuel Herrendorf, Christian Komusiewicz, Nils Morawietz +1

Network sparsification is the task of reducing the number of edges of a given graph while preserving some crucial graph property. In community-aware network sparsification, the pre…

cs.DS2022★ 1 cited

Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings

Niels Grüttemeier, Christian Komusiewicz, Nils Morawietz

In Bayesian Network Structure Learning (BNSL), one is given a variable set and parent scores for each variable and aims to compute a DAG, called Bayesian network, that maximizes th…