New Frontiers

In the previous chapters, we overviewed many of the tools and applications of network science within analytics projects. In this chapter, we’ll look ahead toward the newer tools being developed that have many promising applications within network science, including quantum graph algorithms, deep learning/large language model architecture optimization, and multilevel graphs that are useful for organizing metadata and understanding genetics data.

While the prior chapters included coded examples, this chapter will focus on ideas and the possibilities for development in the future. Network science is an evolving field, and it’s likely that tools we can’t even imagine right now will be commonplace in the next decade. Let’s dive into some of the newer applications and see how network science continues to contribute to knowledge in many different fields.

Specifically, we will cover the following topics:

• Quantum network science algorithms...