The purpose of structure learning is to construct a qualitative relationship of Bayesian networks. Bayesian network with interpretability and logicality is widely applied in a lot of fields. With the extensive development of high-dimensional and low sample size data in some applications, structure learning of Bayesian networks for high dimension and low sample size data becomes a challenging problem. To handle this problem, we propose a method for learning high-dimensional Bayesian network structures based on multi-granularity information. First, an undirected independence graph construction method containing global structure information is designed to optimize the search space of network structure. Then, an improved agglomerative hierarchical clustering method is presented to cluster variables into sub-granules, which reduces the complexity of structure learning by considering the variable community characteristic in high-dimensional data. Finally, the corresponding sub-graphs are formed by learning the internal structure of sub-granules, and the final network structure is constructed based on the proposed construct link graph algorithm. To verify the proposed method, we conduct two types of comparison experiments: comparison experiment and embedded comparison experiment. The results of the experiments show that our approach is superior to the competitors. The results indicate that our method can not only learn structures of Bayesian network from high-dimensional data efficiently but also improve the efficiency and accuracy of network structure generated by other algorithms for high-dimensional data.

结构学习的目的是构建贝叶斯网络的定性关系。具有可解释性和逻辑性的贝叶斯网络被广泛应用于许多领域。随着高维低样本数据在一些应用中的广泛发展，贝叶斯网络对高维低样本数据的结构学习成为一个具有挑战性的问题。为了解决这个问题，我们提出了一种基于多粒度信息学习高维贝叶斯网络结构的方法。首先，设计了一种包含全局结构信息的无向独立图构建方法来优化网络结构的搜索空间。然后，提出了一种改进的凝聚层次聚类方法将变量聚类为子颗粒，通过考虑高维数据中的可变社区特征，降低了结构学习的复杂性。最后通过学习子颗粒的内部结构形成相应的子图，并基于提出的构造链接图算法构建最终的网络结构。为了验证所提出的方法，我们进行了两种比较实验：比较实验和嵌入式比较实验。实验结果表明我们的方法优于竞争对手。结果表明，我们的方法不仅可以有效地从高维数据中学习贝叶斯网络的结构，而且可以提高其他算法对高维数据生成的网络结构的效率和准确性。