3 citations · 5 across the 6 of their papers we have counts for
11 papers
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…
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…
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…
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…
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…
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…
Local Search for Group Closeness Maximization on Big Graphs
Eugenio Angriman, Alexander van der Grinten, Henning Meyerhenke
In network analysis and graph mining, closeness centrality is a popular measure to infer the importance of a vertex. Computing closeness efficiently for individual vertices receive…
Group Centrality Maximization for Large-scale Graphs
Eugenio Angriman, Alexander van der Grinten, Aleksandar Bojchevski +3
The study of vertex centrality measures is a key aspect of network analysis. Naturally, such centrality measures have been generalized to groups of vertices; for popular measures i…
Guidelines for Experimental Algorithmics in Network Analysis
Eugenio Angriman, Alexander van der Grinten, Moritz von Looz +4
The field of network science is a highly interdisciplinary area; for the empirical analysis of network data, it draws algorithmic methodologies from several research fields. Hence,…
Parallel Adaptive Sampling with almost no Synchronization
Alexander van der Grinten, Eugenio Angriman, Henning Meyerhenke
Approximation via sampling is a widespread technique whenever exact solutions are too expensive. In this paper, we present techniques for an efficient parallelization of adaptive (…
Computing Top-k Closeness Centrality in Fully-dynamic Graphs
Patrick Bisenius, Elisabetta Bergamini, Eugenio Angriman +1
Closeness is a widely-studied centrality measure. Since it requires all pairwise distances, computing closeness for all nodes is infeasible for large real-world networks. However,…