A bipartite network is a graph structure where nodes are from two distinct domains and only inter-domain interactions exist as edges. A large number of network embedding methods exist to learn vectorial node representations from general graphs with both homogeneous and heterogeneous node and edge types, including some that can specifically model the distinct properties of bipartite networks. However, these methods are inadequate to model multiplex bipartite networks (e.g., in e-commerce), that have multiple types of interactions (e.g., click, inquiry, and buy) and node attributes. Most real-world multiplex bipartite networks are also sparse and have imbalanced node distributions that are challenging to model. In this paper, we develop an unsupervised Dual HyperGraph Convolutional Network (DualHGCN) model that scalably transforms the multiplex bipartite network into two sets of homogeneous hypergraphs and uses spectral hypergraph convolutional operators, along with intra- and inter-message passing strategies to promote information exchange within and across domains, to learn effective node embedding. We benchmark DualHGCN using four real-world datasets on link prediction and node classification tasks. Our extensive experiments demonstrate that DualHGCN significantly outperforms state-of-the-art methods, and is robust to varying sparsity levels and imbalanced node distributions.

本文提出了一种无监督的Dual HyperGraph Convolutional Network (DualHGCN)模型，通过将多重二分网络转化为两组同质超图，并使用谱超图卷积算子以及内部和跨域间的信息传递策略来学习有效的节点嵌入，对四个真实数据集进行基准测试，展示了 DualHGCN 显著优于现有最先进方法，并且对不同的稀疏水平和不平衡节点分布具有鲁棒性。

双超图卷积神经网络应用于多路二分网络嵌入