It's just a more efficient way to do a form of graph matching[1] on a dynamic graph (one that evolves over time). There wasn't really a pure closed form mathem

It's just a more efficient way to do a form of graph matching[1] on a dynamic graph (one that evolves over time). There wasn't really a pure closed form mathematic solution to the problem like we can on static graphs (Gale-Shapely algorithm for one). You find papers like this one [2] with heavy math and some sort of proposed solution. So this type of problem is a really a good fit for machine learning to propose solutions. 1. https://en.wikipedia.org/wiki/Graph_matching https://en.wikipedia.org/wiki/Graph_matching 2. https://www.semanticscholar.org/paper/Thickness-and-Information-in-Dynamic-Matching-Akbarpour-Li/9de8f77c5d2c31988efac77f1bd29ddd6ddd0cfc https://www.semanticscholar.org/paper/Thickness-and-Informat...