A. van der Grinten and H. Meyerhenke. Scaling Betweenness Approximation to Billions of Edges by MPI-based Adaptive Sampling. 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), New Orleans, LA, USA, May 18-22, 2020, IEEE, 2020.

E. Angriman, M. Predari, A. van der Grinten, and H. Meyerhenke. Approximation of the Diagonal of a Laplacian's Pseudoinverse for Complex Network Analysis. 28th Annual European Symposium on Algorithms, ESA 2020, September 7-9, 2020, Pisa, Italy (Virtual Conference), Schloss Dagstuhl - Leibniz-Zentrum für Informatik, volume 173, 2020.

A. van der Grinten, E. Angriman, and H. Meyerhenke. Scaling up network centrality computations - {A} brief overview. it Inf. Technol., volume 62, issue 34, 2020.

C. Tzovas, M. Predari, and H. Meyerhenke. Distributing Sparse Matrix/Graph Applications in Heterogeneous Clusters - an Experimental Study. 27th {IEEE} International Conference on High Performance Computing, Data, and Analytics, HiPC 2020, Pune, India, December 16-19, 2020, IEEE, 2020.

E. Angriman, A. van der Grinten, A. Bojchevski, D. Zügner, S. Günnemann, and H. Meyerhenke. {G}roup {C}entrality {M}aximization for {L}arge-scale {G}raphs. Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, SIAM, 2020.

M. F. Faraj, A. van der Grinten, H. Meyerhenke, J. L. Träff, and C. Schulz. {H}igh-{Q}uality {H}ierarchical {P}rocess {M}apping. CoRR, volume 200107134, 2020.

M. Simsek and H. Meyerhenke. {C}ombined {C}entrality {M}easures for an {I}mproved {C}haracterization of {I}nfluence {S}preadin {S}ocial {N}etworks. CoRR, volume 200305254, 2020.