| Guidelines for experimental algorithmics: A case study in network analysis E Angriman, A van der Grinten, M von Looz, H Meyerhenke, M Nöllenburg, ... Algorithms 12 (7), 127, 2019 | 29 | 2019 |
| Computing Top-k Closeness Centrality in Fully-dynamic Graphs P Bisenius, E Bergamin, E Angriman, H Meyerhenke 2018 Proceedings of the Twentieth Workshop on Algorithm Engineering and …, 2018 | 26 | 2018 |
| Group centrality maximization for large-scale graphs E Angriman, A van der Grinten, A Bojchevski, D Zügner, S Günnemann, ... 2020 Proceedings of the twenty-second workshop on Algorithm Engineering and …, 2020 | 22 | 2020 |
| Scaling up network centrality computations–A brief overview A van der Grinten, E Angriman, H Meyerhenke it-Information Technology 62 (3-4), 189-204, 2020 | 17 | 2020 |
| Algorithms for large-scale network analysis and the NetworKit toolkit E Angriman, A van der Grinten, M Hamann, H Meyerhenke, M Penschuck Algorithms for Big Data: DFG Priority Program 1736, 3-20, 2023 | 13 | 2023 |
| Approximation of the diagonal of a laplacian's pseudoinverse for complex network analysis E Angriman, M Predari, A van der Grinten, H Meyerhenke arXiv preprint arXiv:2006.13679, 2020 | 13 | 2020 |
| Group-Harmonic and Group-Closeness Maximization–Approximation and Engineering∗ E Angriman, R Becker, G d'Angelo, H Gilbert, A van Der Grinten, ... 2021 Proceedings of the Workshop on Algorithm Engineering and Experiments …, 2021 | 12 | 2021 |
| New approximation algorithms for forest closeness centrality–for individual vertices and vertex groups A van der Grinten, E Angriman, M Predari, H Meyerhenke Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021 | 11 | 2021 |
| Local search for group closeness maximization on big graphs E Angriman, A van der Grinten, H Meyerhenke 2019 IEEE International Conference on Big Data (Big Data), 711-720, 2019 | 9 | 2019 |
| Parallel adaptive sampling with almost no synchronization A Grinten, E Angriman, H Meyerhenke European Conference on Parallel Processing, 434-447, 2019 | 7 | 2019 |
| Fully-dynamic weighted matching approximation in practice E Angriman, H Meyerhenke, C Schulz, B Uçar SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21), 32-44, 2021 | 5 | 2021 |
| Computing top-k closeness centrality in fully dynamic graphs E Angriman, P Bisenius, E Bergamini, H Meyerhenke Massive Graph Analytics, 161-192, 2022 | 3 | 2022 |
| A Batch-dynamic Suitor Algorithm for Approximating Maximum Weighted Matching E Angriman, M Boroń, H Meyerhenke ACM Journal of Experimental Algorithmics (JEA) 27 (1), 1-41, 2022 | 2 | 2022 |
| Interactive Visualization of Protein RINs using NetworKit in the Cloud E Angriman, F Brandt-Tumescheit, L Franke, A van der Grinten, ... 2022 IEEE International Parallel and Distributed Processing Symposium …, 2022 | 1 | 2022 |
| Parallel Adaptive Sampling with almost no Synchronization A van der Grinten, E Angriman, H Meyerhenke arXiv preprint arXiv:1903.09422, 2019 | 1 | 2019 |
| Efficient computation of Harmonic Centrality on large networks: theory and practice E Angriman | 1 | 2016 |
| Scalable Algorithms for the Analysis of Massive Networks E Angriman PQDT-Global, 2021 | | 2021 |
| 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)| 978-1-6654-9747-3/22/$31.00© 2022 IEEE| DOI: 10.1109/IPDPSW55747. 2022.00225 TS Abdelrahman, GS Abhishek, S Abraham, JA Acosta, S Adavally, ... | | |
| IPDPS 2018 Outside Reviewers Y Akhremtsev, E Angriman, B Archibald, CEA Cédric Augonnet, ... | | |
| Three Families of Optimization Problems Related to Network Centrality E Angriman, A van der Grinten, M Predari, H Meyerhenke Group 5 (10), 50-100, 0 | | |
