Dr. Eugenio Angriman
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Mail Address: |
Humboldt University Berlin |
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Phone: |
030 2093 3831 |
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E-mail: |
angrimae (at) hu-berlin (dot) de |
Research interests
Main research interests concern scalable algorithms for large and complex networked systems:
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Algorithmic analysis of large complex networks
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Identification of top-k most central nodes in large networks
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Centrality metrics for groups of nodes
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Shared memory parallelism
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Algorithms for dynamic networks
Publications
- E. Angriman, M. Boroń, H. Meyerhenke: A Batch-Dynamic Suitor Algorithm for Approximating Maximum Weighted Matching - accepted at ACM JEA
- E. Angriman, F. Brandt-Tumescheit, L. Franke, A. van der Grinten, H. Meyerhenke: Interactive Visualization of Protein RINs using NetworKit in the Cloud - accepted at IPDPS GrAPL 2022
- E. Angriman, P. Bisenius, E. Bergamini, H. Meyerhenke: Computing Top-k Closeness Centrality in Fully - Dynamic Graphs - accepted for publication as a chapter in Massive Graph Analytics
- E. Angriman, A. van der Grinten, M. Hamann, H. Meyerhenke, M. Penschuck: Algorithms for Large-scale Network Analysis and the NetworKit Toolkit - chapter of "Algorithms for Big Data - DFG Priority Program 1736", to be published in 2022
- E. Angriman, H. Meyerhenke, C. Schulz and B. Uçar: Fully-dynamic Weighted Matching Approximation in Practice - accepted at ACDA 2021
- A. van der Grinten, E. Angriman, M. Predari, and H. Meyerhenke: New Approximation Algorithms for Forest Closeness Centrality - for Individual Vertices and Vertex Groups - accepted at SDM 2021
- E. Angriman, R. Becker, G. D'Angelo, H. Gilbert, A. van der Grinten and H. Meyerhenke: Group Harmonic and Group Closeness Maximization - Approximation and Engineering - accepted at ALENEX 2021
- E. Angriman, A. van der Grinten, M. Predari, and H. Meyerhenke: Approximation of the Diagonal of a Laplacian's Pseudoinverse for Complex Network Analysis - accepted at ESA 2020 Track B
- A. van der Grinten, E. Angriman, H. Meyerhenke: Scaling up network centrality computations - A brief overview - accepted at Information Technology
- E. Angriman, A. van der Grinten, A. Bojchevski, D. Zügner, S. Günnemann, H. Meyerhenke: Group Centrality Maximization for Large-scale Graphs - accepted at ALENEX 2020
- E. Angriman, A. van der Grinten, H. Meyerhenke: Local Search for Group Closeness Maximization on Big Graphs - accepted at IEEE BigData 2019
- E. Angriman, A. van der Grinten, M. von Looz, H. Meyerhenke, M. Nöllenburg, M. Predari, C. Tzovas: Guidelines for Experimental Algorithmics: A Case Study in Network Analysis - accepted at MDPI Algorithms
- A. van der Grinten, E. Angriman, H. Meyerhenke: Parallel Adaptive Sampling with almost no Synchronization - accepted at Euro-Par 2019
- P. Bisenius, E. Bergamini, E. Angriman, H. Meyerhenke: Computing Top-k Closeness Centrality in Fully - Dynamic Graphs - accepted at ALENEX 2018