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Repository made for didactic purposes. We reproduce some of the results of the research article: "Albert, R., Jeong, H. & Barabási, AL. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000). https://doi.org/10.1038/35019019".

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Net-Tolerance

Repository made for didactic purposes. We reproduce some of the results of the research article: "Albert, R., Jeong, H. & Barabási, AL. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000). https://doi.org/10.1038/35019019".


What's inside this repository?

  1. README.md: A markdown file that explains the content of the repository.

  2. modules/: A folder including 2 Python modules used to construct random networks using the Erdös-Renyi model and scale-free networks using the Barabási-Albert model. The files included are:

    • __init__.py: A init file that allows us to import the modules into our Jupyter Notebook.

    • random.py: A Python file including the RandomNetwork class used to build random networks using the Erdös-Renyi model.

    • scale_free.py: A Python file including the ScaleFree class used to build scale-free networks using the Barabási-Albert model.

  3. source/: A folder including 4 Python modules used to make the plots presented in "Albert, R., Jeong, H. & Barabási, AL. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000). https://doi.org/10.1038/35019019". The files included are:

    • __init__.py: A init file that allows us to import the modules into our Jupyter Notebook.

    • initial_plots.py: A Python file including functions that help obtain Fig. 1 of "Albert, R., Jeong, H. & Barabási, AL. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000). https://doi.org/10.1038/35019019" along with their degree distribution plots.

    • diameter.py: A Python file including functions that help obtain Fig. 2 of "Albert, R., Jeong, H. & Barabási, AL. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000). https://doi.org/10.1038/35019019".

    • components.py: A Python file including functions that help obtain Fig. 3 of "Albert, R., Jeong, H. & Barabási, AL. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000). https://doi.org/10.1038/35019019".

    • cluster_dist.py: A Python file including functions that help obtain Fig. 4 of "Albert, R., Jeong, H. & Barabási, AL. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000). https://doi.org/10.1038/35019019".

  4. images/: A folder including the obtained plots.

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Repository made for didactic purposes. We reproduce some of the results of the research article: "Albert, R., Jeong, H. & Barabási, AL. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000). https://doi.org/10.1038/35019019".

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