Chain graphs(CGs) including both Bayesian networks(BNs) and Markov networks(MNs) as special cases

can express more independence models compared to the basic probabilistic graphical models.Today there exist several researches on structure learning of chain graphs.In this paper

we propose an algorithm for learning the equivalence classes of a chain graph based on the Grow-Shrink(GS) algorithm for structure learning of Bayesian networks.The algorithm works by first learning the adjacent nodes of each node in a chain graph for recovering its skeleton

and then discovering its complexes using the conditional independence tests and the property of the complexes.Theoretical analysis and experimental results demonstrate the effectiveness and correctness of the proposed algorithm.