Thank you for making it this far! We hope you've enjoyed the book. If you want to further your learning, here's a few resources to keep you going.
"Statistical Analysis of Network Data" is an incredible resource for learning how to analyze graph data from a statistical viewpoint. It is written by Boston University's professor of mathematics Eric D. Kolaczyck. I used it during graduate school as part of my personnal learning journey. The book's website can be found here, and is available on Amazon (click on the book link below).
This is a book by Prof. Albert-Laszlo Barabasi, and is freely available online. In it, he explores network analysis from the perspective of an applied academic discipline, showing universal properties and processes that underly networks.
This is a book by Prof. Allen Downey at the Olin College of Engineering. In fact, this was the first book that exposed me (Eric Ma) to network science and its ideas, which thus inspired my thesis topic, which then gave me the impetus to learn graph theory and make this tutorial. I hope it becomes a useful thing for you too. You can find the book at Green Tea Press for free, but do consider purchasing a copy to support Allen's work!
This is a book by UCSD Prof. Fan Chung. It is being partially revised, with chapters available online. Contains valuable information on the connections between graphs and linear algebra.
Snacks is a repository of network analysis learning tools curated in the same spirit as the "Awesome-X" repositories that show up on GitHub. You can find it here.