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}

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  keywords = {machine learning, neurips, reproducibility challenge}
}

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  type = {Editorial},
  language = {},
  domain = {},
  keywords = {machine learning, neurips, reproducibility challenge}
}

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  type = {Editorial},
  language = {},
  domain = {},
  keywords = {machine learning, ICLR, reproducibility challenge}
}

@inproceedings{khajehnejad2022crosswalk,
  title={Crosswalk: Fairness-enhanced node representation learning},
  author={Khajehnejad, Ahmad and Khajehnejad, Moein and Babaei, Mahmoudreza and Gummadi, Krishna P and Weller, Adrian and Mirzasoleiman, Baharan},
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  pages={11963--11970},
  year={2022}
}
@article{barabasi,
author = {Albert-László Barabási  and Réka Albert },
title = {Emergence of Scaling in Random Networks},
journal = {Science},
volume = {286},
number = {5439},
pages = {509-512},
year = {1999},
doi = {10.1126/science.286.5439.509},
URL = {https://www.science.org/doi/abs/10.1126/science.286.5439.509},
eprint = {https://www.science.org/doi/pdf/10.1126/science.286.5439.509},
abstract = {Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.}}