Graph model reasoning is an important work in graph model research. How to verify the designed reasoning algorithm needs to be tested based on a large number of experimental samples. In order to verify the effectiveness and solution time of the optimal query algorithm, the experimental data designed is critical. In this paper, the random graph model is designed according to the structural characteristics and parameter characteristics of the graph model. The algorithm designed in this paper generates CP-nets of random structure according to the number and degree of vertices. The principle is getting DAG code by improving Prüfer code, furthermore, the one-to-one mapping between DAG code and graph structure is established to realize the random generation of graph model. Then, by combining the designed dominant query algorithm with the typical dominant query. It is proved that the graph model generated by the designed graph model generation algorithm has the randomness of corresponding characteristics. The time consumption of the dominant detection algorithm is heavily dependent on the randomness of topological structure of the graph and the number of parameters.

CP-nets graphic model; DAG code; Prüfer code; randomness; dominant query; artificial intelligence