5 citations · 7 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Scalable Katz Ranking Computation in Large Static and Dynamic Graphs
Alexander van der Grinten, Elisabetta Bergamini, Oded Green +2
Network analysis defines a number of centrality measures to identify the most central nodes in a network. Fast computation of those measures is a major challenge in algorithmic net…