About the Graph Algorithms Book

How important are graph algorithms? Could a single graph algorithm really be worth $350 million? Yes, it can!

When Larry Page was a student at Stanford he created the PageRank algorithm for predicting how high a page should be ranked in a search result. The patent, which was owned by Stanford was licensed to Google for stock. When Google went public the stock was valued at $350 million!

This story is just one example of why graph algorithms are relevant to modeling the world around us. In the graph community, we have the expression "graphs are everywhere." This statement reflects the fact that graph data models, particularly the labeled property graph model.

Focus on Interactive Simulations

The reason this book was created was to address the lack of an easy-to-customize interactive website on graph algorithms that includes modern work on graph machine learning. Our goal is to allow anyone who wants to teach graph algorithms the ability to use this content for their courses.

Our focus is not to just describe the algorithms with descriptive text but to allow students to have fine-grain control of animations of the algorithms in action. In the past, this would be too expensive to create and maintain these animations. However, with the assistance of generative AI tools, these animations are easy to create, easy to customize and easy to maintain.

Acknowledgments

I want to thank my colleagues, Arun Batchu(https://www.linkedin.com/in/arunbatchu/), Parker Erickson and Jon Herke for their encouragement in the creation of this website.

Much of the work on building small simulations and animations (MicroSims) were created by Val Lockhart and Troy Peterson. Our work leverages their innovations and we are grateful for their work.

I also want to acknowledge all the people who contributed to open-source libraries such as p5.js, vis.js and the D3 Network Graph. This website would not be possible without these tools.