187 citations · 581 across the 39 of their papers we have counts for
5 papers · 1 filter
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…
Scaling Betweenness Approximation to Billions of Edges by MPI-based Adaptive Sampling
Alexander van der Grinten, Henning Meyerhenke
Betweenness centrality is one of the most popular vertex centrality measures in network analysis. Hence, many (sequential and parallel) algorithms to compute or approximate between…
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 (…