The article is aimed at generalizing the concepts of a derivative graph and a primitive graph for graphs with trunk connectivity. Theorems are formulated and proved on the main connectedness of the graph of the derivative and on the graph of the antiderivative main connected graphs. The theoretical and practical significance lies in the study of structures that will be best suited for working with algebraic Bayesian networks and, thus, become one of the goal of their machine learning. We note the novelty of looking at the problem, or rather, studying the question for which families of graphs there is a set of loads, the family of MGS over which exactly coincides with the given one.