互信息与多条元路径融合的异质网络表示学习方法

异质信息网络能够对真实世界的诸多复杂应用场景进行建模,其表示学习研究也得到了众多学者的广泛关注.现有的异质网络表示学习方法大多基于元路径来捕获网络中的结构和语义信息,已经在后续的网络分析任务中取得很好的效果.然而,此类方法忽略了元路径的内部节点信息和不同元路径实例的重要性;仅能捕捉到节点的局部信息.因此,提出互信息与多条元路径融合的异质网络表示学习方法.首先,利用一种称为关系旋转编码的元路径内部编码方式,基于相邻节点和元路径上下文节点捕获异质信息网络的结构和语义信息,采用注意力机制来建模各元路径实例的重要性;然后,提出一种互信息最大化与多条元路径融合的无监督异质网络表示学习方法,使用互信息捕获全局信息以及全局信息和局部信息之间的联系.最后,在两个真实数据集上进行实验,并与当前主流的算法进行比较分析.结果表明,所提方法在节点分类和聚类任务上性能都有提升,甚至和一些半监督算法相比也表现出强劲性能.

Heterogeneous Network Representation Learning Method Fusing Mutual Information and Multiple Meta-paths

Heterogeneous information networks can be used for modeling several applications in the real world. Their representation learning has received extensive attention from scholars. Most of the representation learning methods extract structural and semantic information based on meta-paths and their effectiveness in network analysis have been proved. However, these methods ignore the node internal information and different degrees of importance of meta-path instances. Besides, they can capture only the local node information. Thus, this study proposes a heterogeneous network representation learning method fusing mutual information and multiple meta-paths. First, a meta-path internal encoding method called relational rotation encoding is used, which captures the structural and semantic information of the heterogeneous information network according to adjacent nodes and meta-path context nodes. It uses an attention mechanism to model the importance of each meta-path instance. Then, an unsupervised heterogeneous network representation learning method fusing mutual information maximization and multiple meta-paths is proposed and mutual information can capture both global and local information. Finally, experiments are conducted on two real datasets. Compared with the current mainstream algorithms as well as some semi-supervised algorithms, the results show that the proposed method has better performance on node classification and clustering.