activity
20092015
most citedKernelization Using Structural Parameters on Sparse Graph Classes

13 citations · 48 across the 11 of their papers we have counts for

collaborators

13 papers

cs.DS2015★ 6 cited

Fast Biclustering by Dual Parameterization

Pål Grønås Drange, Felix Reidl, Fernando Sánchez Villaamil +1

We study two clustering problems, Starforest Editing, the problem of adding and deleting edges to obtain a disjoint union of stars, and the generalization Bicluster Editing. We sho…

cs.SI2014★ 5 cited

Overlapping Communities in Social Networks

Jan Dreier, Philipp Kuinke, Rafael Przybylski +3

Complex networks can be typically broken down into groups or modules. Discovering this "community structure" is an important step in studying the large-scale structure of networks.…

cs.DS2014

Kernelization and Sparseness: the case of Dominating Set

Pål Grønås Drange, Markus S. Dregi, Fedor V. Fomin +9

We prove that for every positive integer and for every graph class of bounded expansion, the -Dominating Set problem admits a linear kernel on graphs from $\mat…

cs.SI2014

Structural Sparsity of Complex Networks: Bounded Expansion in Random Models and Real-World Graphs

Erik D. Demaine, Felix Reidl, Peter Rossmanith +3

This research establishes that many real-world networks exhibit bounded expansion, a strong notion of structural sparsity, and demonstrates that it can be leveraged to design effic…

cs.DS2014★ 3 cited

A Faster Parameterized Algorithm for Treedepth

Felix Reidl, Peter Rossmanith, Fernando Sanchez Villaamil +1

The width measure \emph{treedepth}, also known as vertex ranking, centered coloring and elimination tree height, is a well-established notion which has recently seen a resurgence o…

cs.DS2013★ 13 cited

Kernelization Using Structural Parameters on Sparse Graph Classes

Jakub Gajarský, Petr Hliněný, Jan Obdržálek +5

Meta-theorems for polynomial (linear) kernels have been the subject of intensive research in parameterized complexity. Heretofore, meta-theorems for linear kernels exist on graphs…