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Area of Science:

  • Social Network Analysis
  • Network Science
  • Data Science

Background:

  • Network measures typically require complete population data, which is rarely available in practice.
  • Limited understanding exists regarding the reliability of network measures when data is incomplete.

Purpose of the Study:

  • To investigate the impact of missing node data on four classes of network measures: centrality, centralization, topology, and homophily.
  • To assess the robustness of various network metrics under different levels of data missingness.

Main Methods:

  • Utilized a Monte Carlo simulation process on 12 diverse empirical networks.
  • Generated data with controlled levels of missingness to compare network scores against known baseline values.

Main Results:

  • Measurement bias generally increases with higher proportions of missing data.
  • Sensitivity to missing data varies significantly across network measures (e.g., betweenness and Bonacich centralization are sensitive; closeness and in-degree are robust).
  • Network topology measures like transitivity showed minimal bias, while others like tau statistic and distance were difficult to capture accurately.

Conclusions:

  • The effect of missing data on network measurement is context-dependent, influenced by both the specific network measure and the network's structural features.
  • Larger and more centralized networks demonstrate greater robustness to missing data, particularly for centrality and centralization measures.
  • Network cohesiveness influences robustness differently depending on the measure, affecting topological features less than centralization.