efficiency-networks

This repository includes the code for generating the synthetic weighted networks used in our work "Quantifying efficient information exchange in real network flows".

https://github.com/gbertagnolli/efficiency-networks

Science Score: 54.0%

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  • CITATION.cff file
    Found CITATION.cff file
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    Found 3 DOI reference(s) in README
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    Links to: arxiv.org
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    Low similarity (4.6%) to scientific vocabulary
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Repository

This repository includes the code for generating the synthetic weighted networks used in our work "Quantifying efficient information exchange in real network flows".

Basic Info
  • Host: GitHub
  • Owner: gbertagnolli
  • License: mit
  • Language: HTML
  • Default Branch: main
  • Size: 10.5 MB
Statistics
  • Stars: 3
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 5 years ago · Last pushed about 4 years ago
Metadata Files
Readme License Citation

README.md

efficiency-networks

This repository includes the code used in our work Bertagnolli, G., Gallotti, R., & De Domenico, M. (2020). Quantifying efficient information exchange in real network flows. preprint arXiv:2003.11374.

In particular:

If you use the code, or data, please cite our work 1.Bertagnolli, G., Gallotti, R. & De Domenico, M. Quantifying efficient information exchange in real network flows. Commun Phys 4, 1–10 (2021). doi: 10.1038/s42005-021-00612-5, preprint arXiv:2003.11374._ The .bib of the paper is also included in the repo.

Owner

  • Name: Giulia
  • Login: gbertagnolli
  • Kind: user
  • Location: Bolzano, Italy
  • Company: Free University of Bozen-Bolzano

Junior Assistant Professor at the Free University of Bozen (IT), passionate about geometries, stats, data, and (open) code.

Citation (citation.bib)

@misc{bertagnolli2020quantifying,
      title={Quantifying efficient information exchange in real network flows}, 
      author={Giulia Bertagnolli and Riccardo Gallotti and Manlio De Domenico},
      year={2020},
      eprint={2003.11374},
      archivePrefix={arXiv},
      primaryClass={physics.soc-ph}
}

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