Henning Meyerhenke
Dados Biográficos
| ID | 1752501 |
|---|---|
| NOME | Henning Meyerhenke |
| PRENOMES | Henning |
| SOBRENOME | Meyerhenke |
| ASSINATURA | MEYERHENKE H |
| AFILIAÇÕES | Karlsruhe Institute of Technology |
| ORCID | 0000-0002-7769-726X |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 5 |
| TOTAL DE CITAÇÕES | 1 |
| OBRAS COMO AUTOR | 5 |
| OBRAS COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2014 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2023 |
| ÍNDICE H | 1 |
Greedy optimization of resistance-based graph robustness with global and local edge insertions
The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph G . We consider two optimization problems of adding k new edges to G such that the resulting graph has minimal total effective resistance (i.e., is most robust)—one where the new edges can be anywhere in the graph and one where the new edges need to be incident to a specified focus node. The total effective resistance and effective resistanc…
Structure-preserving sparsification methods for social networks
An empirical comparison of Big Graph frameworks in the context of network analysis
NetworKit
We introduce NetworKit, an open-source software package for analyzing the structure of large complex networks. Appropriate algorithmic solutions are required to handle increasingly common large graph data sets containing up to billions of connections. We describe the methodology applied to develop scalable solutions to network analysis problems, including techniques like parallelization, heuristics for computationally expensive problems, efficien…
Benchmarking for Graph Clustering and Partitioning
NetworKit
We introduce NetworKit, an open-source software package for analyzing the structure of large complex networks. Appropriate algorithmic solutions are required to handle increasingly common large graph data sets containing up to billions of connections. We describe the methodology applied to develop scalable solutions to network analysis problems, including techniques like parallelization, heuristics for computationally expensive problems, efficien…
Benchmarking for Graph Clustering and Partitioning
Structure-preserving sparsification methods for social networks
An empirical comparison of Big Graph frameworks in the context of network analysis
NetworKit
We introduce NetworKit, an open-source software package for analyzing the structure of large complex networks. Appropriate algorithmic solutions are required to handle increasingly common large graph data sets containing up to billions of connections. We describe the methodology applied to develop scalable solutions to network analysis problems, including techniques like parallelization, heuristics for computationally expensive problems, efficien…
Greedy optimization of resistance-based graph robustness with global and local edge insertions
The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph G . We consider two optimization problems of adding k new edges to G such that the resulting graph has minimal total effective resistance (i.e., is most robust)—one where the new edges can be anywhere in the graph and one where the new edges need to be incident to a specified focus node. The total effective resistance and effective resistanc…
Complex Network Analysis Techniques (5 obras) · Computer Science (5 obras) · Graph (4 obras) · Theoretical Computer Science (4 obras) · Advanced Graph Neural Networks (3 obras) · Artificial Intelligence (3 obras) · Graph Theory and Algorithms (3 obras) · Algorithm (2 obras) · Cluster analysis (2 obras) · Data mining (2 obras)