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Eugenio Angriman
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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
272019
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
262018
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
192020
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
142020
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
122020
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
112021
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
92021
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
92019
Parallel adaptive sampling with almost no synchronization
A Grinten, E Angriman, H Meyerhenke
European Conference on Parallel Processing, 434-447, 2019
62019
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
42023
Computing top-k closeness centrality in fully dynamic graphs
E Angriman, P Bisenius, E Bergamini, H Meyerhenke
Massive Graph Analytics, 161-192, 2022
32022
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
32021
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
12022
Efficient computation of Harmonic Centrality on large networks: theory and practice
E Angriman
12016
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
2022
Scalable Algorithms for the Analysis of Massive Networks
E Angriman
PQDT-Global, 2021
2021
Parallel Adaptive Sampling with almost no Synchronization
A van der Grinten, E Angriman, H Meyerhenke
arXiv preprint arXiv:1903.09422, 2019
2019
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
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