As a powerful tool for machine learning on the graph, network embedding, which projects nodes into low-dimensional spaces, has a variety of applications on complex networks. Most current methods and models are not suitable for bipartite networks, which have two different types of nodes and there are no links between nodes of the same type. Furthermore, the only existing methods for bipartite network embedding ignore the internal mechanism and highly nonlinear structures of links. Therefore, in this paper, we propose a new deep learning method to learn the node embedding for bipartite networks based on the widely used autoencoder framework. Moreover, we carefully devise a node-level triplet including two types of nodes to assign the embedding by integrating the local and global structures. Meanwhile, we apply the variational autoencoder (VAE), a deep generation model with natural advantages in data generation and reconstruction, to enhance the node embedding for the highly nonlinear relationships between nodes and complex features. Experiments on some widely used datasets show the effectiveness of the proposed model and corresponding algorithm compared with some baseline network (and bipartite) embedding techniques.

作为图上机器学习的强大工具，将节点投影到低维空间中的网络嵌入在复杂网络上具有多种应用。当前的大多数方法和模型均不适用于两部分网络，后者具有两种不同类型的节点，并且相同类型的节点之间没有链接。此外，仅存在的用于二分网络嵌入的方法忽略了链接的内部机制和高度非线性的结构。因此，在本文中，我们提出了一种新的深度学习方法，该方法基于广泛使用的自动编码器框架来学习双向网络的节点嵌入。此外，我们精心设计了一个节点级三元组，其中包括两种类型的节点，以通过集成局部和全局结构来分配嵌入。同时，我们应用变分自动编码器（VAE）在数据生成和重构中具有自然优势的深层生成模型，可以增强节点的嵌入，以实现节点与复杂特征之间的高度非线性关系。在一些广泛使用的数据集上进行的实验表明，与某些基线网络（和二分体）嵌入技术相比，该模型和相应算法的有效性。