4 citations · 14 across the 15 of their papers we have counts for
16 papers · 1 filter
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…
High-Quality Hierarchical Process Mapping
Marcelo Fonseca Faraj, Alexander van der Grinten, Henning Meyerhenke +2
Partitioning graphs into blocks of roughly equal size such that few edges run between blocks is a frequently needed operation when processing graphs on a parallel computer. When a…
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…