I stumbled across Binary Decision Diagrams (BDDs) by chance. They are an efficient data structure to represent sets of graphs. While a graph $G$ is a set of vertices $V$ along with a set of edges $E$ that connect the vertices, a graph set is a collection of subgraphs over the universe $V$.
In this post, I’ll review a paper from 2018 that deals with generating boolean decision rules and uses column generation. The paper is well worth the read if you are interested in explainable AI models.
Recently, I had the need to compute maximum weighted cliques on very dense large graphs. This is a well studied problem, and a nice survey paper from 90’s by Pardalos and Xue gives a good overview of approaches.