no code implementations • 25 Jul 2023 • Enqiang Zhu, Yu Zhang, Shengzhi Wang, Darren Strash, Chanjuan Liu
Given a graph, the minimum dominating set (MinDS) problem is to identify a smallest set $D$ of vertices such that every vertex not in $D$ is adjacent to at least one vertex in $D$.
no code implementations • 1 Feb 2023 • Roman Erhardt, Kathrin Hanauer, Nils Kriege, Christian Schulz, Darren Strash
We propose improved exact and heuristic algorithms for solving the maximum weight clique problem, a well-known problem in graph theory with many applications.
1 code implementation • 12 Aug 2020 • Alexander Gellner, Sebastian Lamm, Christian Schulz, Darren Strash, Bogdán Zaválnij
A key feature of our work is that some transformation rules can increase the size of the input.
3 code implementations • 23 Apr 2020 • Wolfgang Ost, Christian Schulz, Darren Strash
Many applications rely on time-intensive matrix operations, such as factorization, which can be sped up significantly for large sparse matrices by interpreting the matrix as a sparse graph and computing a node ordering that minimizes the so-called fill-in.
Data Structures and Algorithms Combinatorics
1 code implementation • 17 Feb 2020 • Monika Henzinger, Alexander Noe, Christian Schulz, Darren Strash
We present a practically efficient algorithm that finds all global minimum cuts in huge undirected graphs.
Data Structures and Algorithms
1 code implementation • 20 Oct 2017 • Daniel Funke, Sebastian Lamm, Peter Sanders, Christian Schulz, Darren Strash, Moritz von Looz
Analyzing massive complex networks yields promising insights about our everyday lives.
Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Social and Information Networks
2 code implementations • 21 Aug 2017 • Monika Henzinger, Alexander Noe, Christian Schulz, Darren Strash
The minimum cut problem for an undirected edge-weighted graph asks us to divide its set of nodes into two blocks while minimizing the weight sum of the cut edges.
Data Structures and Algorithms Distributed, Parallel, and Cluster Computing
1 code implementation • 6 Feb 2017 • Peter Sanders, Christian Schulz, Darren Strash, Robert Williger
Computing high quality node separators in large graphs is necessary for a variety of applications, ranging from divide-and-conquer algorithms to VLSI design.
1 code implementation • 20 Sep 2016 • Lukas Barth, Benjamin Niedermann, Martin Nöllenburg, Darren Strash
Operations like rotation, zoom, and translation dynamically change the map over time and make a consistent adaptation of the map labeling necessary.
Computational Geometry Data Structures and Algorithms F.2.2; G.2.2; G.2.3
1 code implementation • 2 Sep 2015 • Sebastian Lamm, Peter Sanders, Christian Schulz, Darren Strash, Renato F. Werneck
To avoid this problem, we recursively choose vertices that are likely to be in a large independent set (using an evolutionary approach), then further kernelize the graph.
1 code implementation • 2 Mar 2011 • David Eppstein, Darren Strash
We implement a new algorithm for listing all maximal cliques in sparse graphs due to Eppstein, L\"offler, and Strash (ISAAC 2010) and analyze its performance on a large corpus of real-world graphs.
Data Structures and Algorithms F.2.2; G.2.2
1 code implementation • 28 Jun 2010 • David Eppstein, Maarten Löffler, Darren Strash
The degeneracy of an $n$-vertex graph $G$ is the smallest number $d$ such that every subgraph of $G$ contains a vertex of degree at most $d$.
Data Structures and Algorithms Discrete Mathematics F.2.2; G.2.2
