  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.
