bdpedigo/networks-course

A short course on network data science at Johns Hopkins University

Network Data Science - EN.580.129 (22)

A repo housing a course introducing students to network data science at Johns Hopkins University.

Term: Intersession 2023

Instructor: Benjamin D. Pedigo

Times: M T W Th F, 1:00PM - 4:15PM

Schedule: 01-09-2023 to 01-20-2023

Credits: 2

Repo structure

• .github: Files specifying how the repo behaves on GitHub
• data: Directory to store raw data.
• docs: Files to build the documentation website in the form of a Jupyter Book
• pkg: A local Python package used for analysis in the Jupyter Notebooks

Building the book

Coming soon

Running the code

Coming soon

Credits

This project is created using the excellent open source Jupyter Book project and the executablebooks/cookiecutter-jupyter-book template.

networks-course's Issues

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`plot_network_partition` reuses variable from outer scope

When working through the community detection part of the material I noticed that the plot_network_partition was using the node_df from the outer scope, which made this function fail when I was applying it on some other data. Not a major thing, but I though I would suggest a fix.

However, I was unable to get the notebook to build since the data isn't in the repo, and hence I couldn't make a PR to fix it. However, I think the code below should fix the issue:

def plot_network_partition(adj, node_data, partition_key):
    fig, axs = plt.subplots(1, 2, figsize=(15, 7))

    networkplot(
        adj,
        x="x",
        y="y",
        node_data=node_data.reset_index(),
        node_alpha=0.9,
        edge_alpha=0.7,
        edge_linewidth=0.4,
        node_hue=partition_key,
        node_size="degree",
        edge_hue="source",
        ax=axs[0],
    )
    _ = axs[0].axis("off")

    _ = heatmap(
        adj,
        inner_hier_labels=node_data[partition_key],
        ax=axs[1],
        cbar=False,
        cmap="Purples",
        vmin=0,
        center=None,
        sort_nodes=True,
    )

    return fig, ax

Excellent materials, btw. Interesting, well-written, and easy to follow. Kudos!

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