DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node
  Classification based on Multi-View Learning and Density Awareness
Jianpeng Liao Jun Yan Qian Tao
Graph-based semi-supervised node classification has been shown to become a
state-of-the-art approach in many applications with high research value and
significance. Most existing methods are only based on the original intrinsic or
artificially established graph structure which may not accurately reflect the
"true" correlation among data and are not optimal for semi-supervised node
classification in the downstream graph neural networks. Besides, while existing
graph-based methods mostly utilize the explicit graph structure, some implicit
information, for example, the density information, can also provide latent
information that can be further exploited. To address these limitations, this
paper proposes the Dual Hypergraph Neural Network (DualHGNN), a new dual
connection model integrating both hypergraph structure learning and hypergraph
representation learning simultaneously in a unified architecture. The DualHGNN
first leverages a multi-view hypergraph learning network to explore the optimal
hypergraph structure from multiple views, constrained by a consistency loss
proposed to improve its generalization. Then, DualHGNN employs a density-aware
hypergraph attention network to explore the high-order semantic correlation
among data points based on the density-aware attention mechanism. Extensive
experiments are conducted in various benchmark datasets, and the results
demonstrate the effectiveness of the proposed approach.