Flattening a 4-D Cube onto Your Desk with Graph Theory

The applicability of graph theory in the context of web2.0 is obvious, with websites like Facebook (undirected graph of friendships) and Twitter (directed graph of followings) built around them. Another obvious application is in computer networks. The field of operations research, which started with applying math to the field of battlefield logistics is chock full of graph theory problems as well. For instance, see <a href="https://medium.com/@rohitpandey576/data-science-in-battle-applying-graph-theory-to-the-ukraine-war-6cda499c86fc" rel="noopener">here</a>. But graph theory is so versatile, it finds applications in data science as well. Apart from the fact that neural networks are special kinds of graphs, it is front and center in the field of combinatorial testing, where we design experiments in a way that important properties or combinations of properties are covered. For instance, see <a href="https://experiencestack.co/using-graph-theory-to-design-experiments-145f24875281" rel="noopener ugc nofollow" target="_blank">here</a>. This is the context in which I first started learning about it. <a href="https://towardsdatascience.com/flattening-a-4-d-cube-onto-your-desk-a4730312d91c">Website</a>