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

1 citations · 1 across the 6 of their papers we have counts for

collaborators

10 papers

cs.LO2026

The Descriptive Complexity of Relation Modification Problems

Florian Chudigiewitsch, Marlene Gründel, Christian Komusiewicz +2

A relation modification problem gets a logical structure and a natural number k as input and asks whether k modifications of the structure suffice to make it satisfy a predefined p…

cs.CC2026

A Parameterized-Complexity Framework for Finding Local Optima

Robert Ganian, Hung P. Hoang, Christian Komusiewicz +1

Local search is a fundamental optimization technique that is both widely used in practice and deeply studied in theory, yet its computational complexity remains poorly understood.…

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.CC2024

Parameterized Local Search for Max -Cut

Jaroslav Garvardt, Niels Grüttemeier, Christian Komusiewicz +1

In the NP-hard Max -Cut problem, one is given an undirected edge-weighted graph and aims to color the vertices of with colors such that the total weight of edges wit…

cs.DS20221 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…