Peter Niemeyer
Biographic Data
| ID | 4163802 |
|---|---|
| NAME | Peter Niemeyer |
| GIVEN NAMES | Peter |
| FAMILY NAME | Niemeyer |
| SIGNATURE | NIEMEYER P |
| AFFILIATIONS | Leuphana University of Lüneburg |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 5 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 1 |
On the impact of network size and average degree on the robustness of centrality measures
Measurement errors are omnipresent in network data. Most studies observe an erroneous network instead of the desired error-free network. It is well known that such errors can have a severe impact on network metrics, especially on centrality measures: a central node in the observed network might be less central in the underlying, error-free network. The robustness is a common concept to measure these effects. Studies have shown that the robustness…
Influence of measurement errors on networks
Most network studies rely on a measured network that differs from the underlying network which is obfuscated by measurement errors. It is well known that such errors can have a severe impact on the reliability of network metrics, especially on centrality measures: a more central node in the observed network might be less central in the underlying network. Previous studies have dealt either with the general effects of measurement errors on central…
Influence of measurement errors on networks
Most network studies rely on a measured network that differs from the underlying network which is obfuscated by measurement errors. It is well known that such errors can have a severe impact on the reliability of network metrics, especially on centrality measures: a more central node in the observed network might be less central in the underlying network. Previous studies have dealt either with the general effects of measurement errors on central…
On the impact of network size and average degree on the robustness of centrality measures
Measurement errors are omnipresent in network data. Most studies observe an erroneous network instead of the desired error-free network. It is well known that such errors can have a severe impact on network metrics, especially on centrality measures: a central node in the observed network might be less central in the underlying, error-free network. The robustness is a common concept to measure these effects. Studies have shown that the robustness…
Influence of measurement errors on networks
Most network studies rely on a measured network that differs from the underlying network which is obfuscated by measurement errors. It is well known that such errors can have a severe impact on the reliability of network metrics, especially on centrality measures: a more central node in the observed network might be less central in the underlying network. Previous studies have dealt either with the general effects of measurement errors on central…
On the impact of network size and average degree on the robustness of centrality measures
Measurement errors are omnipresent in network data. Most studies observe an erroneous network instead of the desired error-free network. It is well known that such errors can have a severe impact on network metrics, especially on centrality measures: a central node in the observed network might be less central in the underlying, error-free network. The robustness is a common concept to measure these effects. Studies have shown that the robustness…
Betweenness centrality (2 works) · Centrality (2 works) · Complex network (2 works) · Complex Network Analysis Techniques (2 works) · Computer Science (2 works) · Katz centrality (2 works) · Mathematics (2 works) · Network science (2 works) · Robustness (evolution (2 works) · Statistics (2 works)