activity
20172022
most citedAlgorithms for Large-scale Network Analysis and the NetworKit Toolkit

3 citations · 5 across the 6 of their papers we have counts for

collaborators

11 papers

cs.SI20223 cited

Algorithms for Large-scale Network Analysis and the NetworKit Toolkit

Eugenio Angriman, Alexander van der Grinten, Michael Hamann +2

The abundance of massive network data in a plethora of applications makes scalable analysis algorithms and software tools necessary to generate knowledge from such data in reasonab…

cs.SI2022

Interactive Visualization of Protein RINs using NetworKit in the Cloud

Eugenio Angriman, Fabian Brandt-Tumescheit, Leon Franke +2

Network analysis has been applied in diverse application domains. In this paper, we consider an example from protein dynamics, specifically residue interaction networks (RINs). In…

cs.DS2021

Fully-dynamic Weighted Matching Approximation in Practice

Eugenio Angriman, Henning Meyerhenke, Christian Schulz +1

Finding large or heavy matchings in graphs is a ubiquitous combinatorial optimization problem. In this paper, we engineer the first non-trivial implementations for approximating th…

cs.DS2021

New Approximation Algorithms for Forest Closeness Centrality -- for Individual Vertices and Vertex Groups

Alexander van der Grinten, Eugenio Angriman, Maria Predari +1

The emergence of massive graph data sets requires fast mining algorithms. Centrality measures to identify important vertices belong to the most popular analysis methods in graph mi…

cs.DS2020

Group-Harmonic and Group-Closeness Maximization -- Approximation and Engineering

Eugenio Angriman, Ruben Becker, Gianlorenzo D'Angelo +3

Centrality measures characterize important nodes in networks. Efficiently computing such nodes has received a lot of attention. When considering the generalization of computing cen…

cs.DS2020

Approximation of the Diagonal of a Laplacian's Pseudoinverse for Complex Network Analysis

Eugenio Angriman, Maria Predari, Alexander van der Grinten +1

The ubiquity of massive graph data sets in numerous applications requires fast algorithms for extracting knowledge from these data. We are motivated here by three electrical measur…