Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes

@article{Neuhuser2021SimulatingSB,
  title={Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes},
  author={Leonie Neuh{\"a}user and Felix I. Stamm and Florian Lemmerich and Michael T. Schaub and Markus Strohmaier},
  journal={Applied Network Science},
  year={2021},
  volume={6},
  pages={1-22}
}
Network analysis provides powerful tools to learn about a variety of social systems. However, most analyses implicitly assume that the considered relational data is error-free, and reliable and accurately reflects the system to be analysed. Especially if the network consists of multiple groups (e.g., genders, races), this assumption conflicts with a range of systematic biases, measurement errors and other inaccuracies that are well documented in the literature. To investigate the effects of… 

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