Graph is one of important interactive visualization tools. In machine learning, it can be built from observational data, to represent pictorially the characteristics of complex systems. Normally, the di erence between graphs can be used for predicting the variance of systems. However, with a small data system, it is hard to describe the real di erence. Therefore, ensemble methods proposed to use multiple models to obtain better predictive performance. In fact, they combine multiple hypotheses to form a better hypothesis that will make good predictions with a particular problem. We propose in this work a new ensemble approach for graph data: multiple hypothesis testing on edges of graph. This paper describes how to use this approach to deal with the problem of comparison of two sets of graph-based models. In order to perform the interests of proposed approach, we experimented on two sets of simulated Bayesian networks.