Influence of measurement errors on networks
Estimating the robustness of centrality measures
Bibliographic Data
| ID | 6161365 |
|---|---|
| Authors | Christoph Martins (0000-0002-3510-0429, Leuphana University of Lüneburg, corresponding author), Christoph Martin, Peter Niemeyer (Leuphana University of Lüneburg) |
| Year | 2019 |
| Volume | 7 |
| Issue | 2 |
| Pages | 180-195 |
| Publication date | 2019-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Network Science (JOURNAL) |
| Journal identifiers | ISSN: 2050-1250 • E-ISSN: 2050-1242 |
| Publisher | Cambridge University Press (PUBLISHER • US) |
| DOI | 10.1017/nws.2019.12 |
| OpenAlex | W3104154397 |
| Language | EN |
| Citations received | 5 |
| References cited | 37 |
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 centrality measures or with the treatment of erroneous network data. In this paper, we propose a method for estimating the impact of measurement errors on the reliability of a centrality measure, given the measured network and assumptions about the type and intensity of the measurement error. This method allows researchers to estimate the robustness of a centrality measure in a specific network and can, therefore, be used as a basis for decision-making. In our experiments, we apply this method to random graphs and real-world networks. We observe that our estimation is, in the vast majority of cases, a good approximation for the robustness of centrality measures. Beyond this, we propose a heuristic to decide whether the estimation procedure should be used. We analyze, for certain networks, why the eigenvector centrality is less robust than, among others, the pagerank. Finally, we give recommendations on how our findings can be applied to future network studies
Betweenness centrality · Centrality · Complex network · Data mining · Katz centrality · Network analysis · Network science · Network theory · PageRank · Robustness (evolution · Statistics · Complex Network Analysis Techniques · Computer Science · Graph theory and applications · Mathematics · Opinion Dynamics and Social Influence · Theoretical Computer Science
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| Unique citing works | 5 |
|---|---|
| Citations per year | 0,83 |
| Citation span | 2020 - 2025 (6) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |