Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Modellierung und Analyse komplexer Systeme

Prof. Dr. Henning Meyerhenke

Henning Meyerhenke

Adresse / Mail Address:

Humboldt-Universität zu Berlin
Institut für Informatik
Unter den Linden 6
D-10099 Berlin

Sitz / Visitor Address:

Rudower Chausse 25, Raum III.303
D-12489 Berlin-Adlershof

Telefon / Phone:

+49 30 2093 41220

E-Mail / Email:

meyerhenke (at)

Brief Curriculum Vitae

Henning Meyerhenke is Professor of Computer Science at Humboldt-Universität zu Berlin since August 2018. Prior to that, he was Professor at University of Cologne and Assistant Professor at Karlsruhe Institute of Technology, respectively. Henning held postdoctoral positions at Georgia Institute of Technology (Atlanta, USA), NEC Laboratories Europe, and University of Paderborn. He received his Diplom degree in Computer Science from Friedrich-Schiller-University Jena in 2004 and his Ph.D. (with highest distinction) in Computer Science from the University of Paderborn in 2008.

Since his time in Karlsruhe, Henning has acquired significant funding from DFG, BMBF, and MWK Baden-Wuerttemberg. Together with his co-authors, he received the Best Algorithms Paper Award at the 22nd IEEE International Parallel and Distributed Processing Symposium (2008) and the Best Paper Award of the 2015 International Symposium on Foundations and Applications of Big Data Analytics.

Research Interests

Henning Meyerhenke's main research interests concern scalable algorithms for large and complex networked systems, in particular for three application areas:

  • Algorithmic analysis of large complex networks
  • Combinatorial scientific computing
  • Applied optimization for algorithmic problems in the natural sciences


A nearly complete and reasonably up-to-date list of my publications can be found at DBLP. We also maintain a list of our publications.

Papers recently accepted but not yet in the DBLP list include:

  • S. Kulagina, H. Meyerhenke, A. Benoit: Mapping Tree-shaped Workflows on Systems with Different Memory Sizes and Processor Speeds. Accepted by Concurrency and Computation: Practice and Experience, June 2023. Special issue of HeteroPar '23.
  • M. Predari, L. Berner, R. Kooij, H. Meyerhenke: Greedy Optimization of Resistance-based Graph Robustness with Global and Local Edge Insertions. Accepted by Social Network Analysis and Mining (SNAM). Special issue of ASONAM '22.
  • Z. Su, H. Meyerhenke, J. Kurths: The climatic interdependence of extreme-rainfall events around the globe. Accepted by Chaos: An Interdisciplinary Journal of Nonlinear Science, AIP (American Institute of Physics) Publishing, March 2022. Preliminary version available at arXiv.
  • E. Angriman, E. Bergamini, P. Bisenius, H. Meyerhenke: Computing Top-k Closeness Centrality in Fully-dynamic Graphs. In Massive Graph Analytics. Taylor & Francis. Conference version appeared at ALENEX'18.
  • M. Wolter, M. von Looz, H. Meyerhenke, C. Jacob: Systematic partitioning of proteins for quantum-chemical fragmentation methods using graph algorithms. Journal of Chemical Theory and Computation.
    [DOI: 10.1021/acs.jctc.0c01054]