Influence of measurement errors on networks: Estimating the robustness of centrality measures

Research output: Journal contributionsJournal articlesResearchpeer-review

Standard

Influence of measurement errors on networks: Estimating the robustness of centrality measures. / Martin, Christoph; Niemeyer, Peter.
In: Network Science, Vol. 7, No. 2, 01.06.2019, p. 180-195.

Research output: Journal contributionsJournal articlesResearchpeer-review

Harvard

APA

Vancouver

Bibtex

@article{8d929abb175c49b687e7697616b755f5,
title = "Influence of measurement errors on networks: Estimating the robustness of centrality measures",
abstract = "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.",
keywords = "Business informatics, centrality measures, measurement error, missing data, robustness",
author = "Christoph Martin and Peter Niemeyer",
year = "2019",
month = jun,
day = "1",
doi = "10.1017/nws.2019.12",
language = "English",
volume = "7",
pages = "180--195",
journal = "Network Science",
issn = "2050-1242",
publisher = "Cambridge University Press",
number = "2",

}

RIS

TY - JOUR

T1 - Influence of measurement errors on networks

T2 - Estimating the robustness of centrality measures

AU - Martin, Christoph

AU - Niemeyer, Peter

PY - 2019/6/1

Y1 - 2019/6/1

N2 - 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.

AB - 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.

KW - Business informatics

KW - centrality measures

KW - measurement error

KW - missing data

KW - robustness

UR - http://www.scopus.com/inward/record.url?scp=85070406775&partnerID=8YFLogxK

U2 - 10.1017/nws.2019.12

DO - 10.1017/nws.2019.12

M3 - Journal articles

AN - SCOPUS:85070406775

VL - 7

SP - 180

EP - 195

JO - Network Science

JF - Network Science

SN - 2050-1242

IS - 2

ER -

Documents

DOI

Recently viewed

Researchers

  1. Lukas Stolz

Publications

  1. Rapid Prototyping of a Mechatronic Engine Valve Controller for IC Engines
  2. Constitutions, Democratic Self-Determination and the Institutional Empowerment of Future Generations: Mitigating an Aporia
  3. Ticio Escobar
  4. Resolving conflicts between people and over time in the transformation toward sustainability
  5. Lyapunov stability analysis to set up a saturating PI controller with anti-windup for a mass flow system
  6. U-model-based dynamic inversion control for quadrotor UAV systems
  7. Systematic risk behavior in cyclical industries
  8. Bridging scenario planning and backcasting
  9. On the Existence of Digital Objects
  10. Release of monomers from four different composite materials after halogen and LED curing
  11. System and action theory
  12. Audio-Hacks
  13. Internet: Impact and Potential for Learning and Instruction
  14. Modeling and Simulation of Electrochemical Cells under Applied Voltage
  15. Controlling a Bank Model Economy by Sliding Mode Control with Help of Kalman Filter
  16. Does ESG performance have an impact on financial performance?
  17. An empirical investigation of experiences and the link between a servicedominant logic mindset, competitive advantage, and performance of nonprofit organizations
  18. How and Why Different Forms of Expertise Moderate Anchor Precision in Price Decisions
  19. An empirical note on commuting distance and sleep during workweek and weekend
  20. Dietary patterns of children on three indigenous societies
  21. Predictive mapping of plant species and communities using GIS and Landsat data in a southern Mongolian mountain range
  22. Der FFB-Server mit Microsoft Windows Server 2003
  23. Recurring patterns and blueprints of industrial symbioses as structural units for an it tool
  24. Shifts in plant functional trait dynamics in relation to soil microbiome in modern and wild barley
  25. Putting Architecture in its Social Space: the Fields and Skills of Planning Maastricht
  26. Detection of oscillations with application in the pantograph control
  27. Revegetation in agricultural areas: the development of structural complexity and floristic diversity
  28. The Integration of Wheelchair Users in Team Handball