I am a researcher in the algorithm engineering group Heidelberg. In my work, I focus on combining machine learning with conventional algorithmic techniques such as local search or branch and bound. The problems of interest are mainly classical NP-hard optimization problems such as Vertex Cover or Graph Coloring.

Contact

• tc353 at uni-heidelberg.de
• +49 6221 54 14344
• Room: 1/332
  Im Neuenheimer Feld 205
  69120 Heidelberg
  Germany

Publications

Peer-reviewed Papers

1. Efficient Minimum Weight Vertex Cover Heuristics Using Graph Neural Networks. Kenneth Langedal, Johannes Langguth, Fredrik Manne, and Daniel Thilo Schroeder. In proceddings of the 20th International Symposium on Experimental Algorithms (SEA 2022). [ link ]
2. PACE Solver Description: Zygosity. Emmanuel Arrighi, Pål Grønås Drange, Kenneth Langedal, Farhad Vadiee, Martin Vatshelle, and Petra Wolf. In proceddings of the 18th International Symposium on Parameterized and Exact Computation (IPEC 2023). [ link ]
3. Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks. Kenneth Langedal, Demian Hespe, and Peter Sanders. In proceddings of the 22nd International Symposium on Experimental Algorithms (SEA 2024). [ link ]
4. PACE Solver Description: LUNCH - Linear Uncrossing Heuristics. Kenneth Langedal, Matthias Bentert, Thorgal Blanco, and Pål Grønås Drange. In proceddings of the 19th International Symposium on Parameterized and Exact Computation (IPEC 2024). [ link ]
5. Graph Neural Networks as Ordering Heuristics for Parallel Graph Coloring. Kenneth Langedal and Fredrik Manne. In proceddings of the Symposium on Algorithm Engineering and Experiments (ALENEX 2025). [ link ]



Technical Reports

6. GNNs for smaller kernels, finding the one rule to reduce them all. Kenneth Langedal, Fredrik Manne, Ernestine Großmann, Christian Schulz, Matthias Schimek, and Fabian Brandt-Tumescheit. In report from Dagstuhl Seminar 23491 on Scalable Graph Mining and Learning (2024). [ link ]
7. A Comprehensive Survey of Data Reduction Rules for the Maximum Weighted Independent Set Problem. Ernestine Großmann, Kenneth Langedal, and Christian Schulz. Technical report (2024). [ link ]
8. Accelerating Reductions Using Graph Neural Networks and a New Concurrent Local Search for the Maximum Weight Independent Set Problem. Ernestine Großmann, Kenneth Langedal, and Christian Schulz. Technical report (2024). [ link ]



Doctoral Thesis

9. Graph Neural Networks in Algorithm Engineering. Kenneth Langedal. The University of Bergen (2025). [ link ]