Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks

TL;DR

This paper introduces DualHGCN, an unsupervised hypergraph convolutional network that effectively embeds multiplex bipartite networks with multiple interaction types, outperforming existing methods in link prediction and node classification.

Contribution

The paper proposes a novel DualHGCN model that transforms multiplex bipartite networks into hypergraphs and employs spectral hypergraph convolution for scalable, robust embedding.

Findings

DualHGCN outperforms state-of-the-art methods in experiments.

The model is robust to network sparsity and node imbalance.

Effective in link prediction and node classification tasks.

Abstract

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.