Beautiful networks and network analytics made simpler with Jupyter

Performing network analytics with NetworkX and Jupyter often results in difficult-to-examine hairballs rather than useful visualizations. Meanwhile, more flexible tools like SigmaJS have high learning curves for people new to JavaScript. Daina Bouquin and John DeBlase share a simple, flexible architecture that can help create beautiful JavaScript networks without ditching the Jupyter Notebook.

The potential uses of network analytics and visualizations are extensive, with applications ranging from social network analysis to environmental science to better understanding how political revolutions spread. However, many of the tools most commonly used for these types of analysis, particularly Python modules like NetworkX, are not designed to produce aesthetically pleasing, interactive visualizations that support the development of theories and inferences. Much of the time, data scientists using tools like Jupyter are left trying to work with network visualizations that look like hairballs or spending a great deal of time trying to use unfamiliar tools like JavaScript or Gephi. Daina Bouquin and John DeBlase share a simple, flexible architecture that can help create beautiful JavaScript networks without ditching the Jupyter Notebook—a side-by-side Jupyter network visualization GUI that allows users to quickly create beautiful visualizations that can help drive research processes. Daina and John then offer a demonstration that illustrates how a librarian can take advantage of this infrastructure to better understand authorship and publishing tendencies in her field.