Hypergraphs play a pivotal role in the modelling of data featuring
higher-order relations involving more than two entities. Hypergraph neural
networks emerge as a powerful tool for processing hypergraph-structured data,
delivering remarkable performance across various tasks, e.g., hypergraph node
classification. However, these models struggle to capture global structural
information due to their reliance on local message passing. To address this
challenge, we propose a novel hypergraph learning framework, HyperGraph
Transformer (HyperGT). HyperGT uses a Transformer-based neural network
architecture to effectively consider global correlations among all nodes and
hyperedges. To incorporate local structural information, HyperGT has two
distinct designs: i) a positional encoding based on the hypergraph incidence
matrix, offering valuable insights into node-node and hyperedge-hyperedge
interactions; and ii) a hypergraph structure regularization in the loss
function, capturing connectivities between nodes and hyperedges. Through these
designs, HyperGT achieves comprehensive hypergraph representation learning by
effectively incorporating global interactions while preserving local
connectivity patterns. Extensive experiments conducted on real-world hypergraph
node classification tasks showcase that HyperGT consistently outperforms
existing methods, establishing new state-of-the-art benchmarks. Ablation
studies affirm the effectiveness of the individual designs of our model.

通过全球节点和超边之间的全球关联以及捕捉节点和超边之间的局部连接性，提出了一种新颖的超图学习框架，称为 HyperGraph Transformer（HyperGT），该框架利用基于 Transformer 的神经网络结构来有效地处理超图结构化数据，实现了全面的超图表示学习，并在超图节点分类任务中取得了出色的性能，超过了现有方法，建立了新的基准水平。

超图变换器用于半监督分类

Hypergraph Transformer for Semi-Supervised Classification

Learning expressive representations for high-dimensional yet sparse features
has been a longstanding problem in information retrieval. Though recent deep
learning methods can partially solve the problem, they often fail to handle the
numerous sparse features, particularly those tail feature values with
infrequent occurrences in the training data. Worse still, existing methods
cannot explicitly leverage the correlations among different instances to help
further improve the representation learning on sparse features since such
relational prior knowledge is not provided. To address these challenges, in
this paper, we tackle the problem of representation learning on feature-sparse
data from a graph learning perspective. Specifically, we propose to model the
sparse features of different instances using hypergraphs where each node
represents a data instance and each hyperedge denotes a distinct feature value.
By passing messages on the constructed hypergraphs based on our Hypergraph
Transformer (HyperFormer), the learned feature representations capture not only
the correlations among different instances but also the correlations among
features. Our experiments demonstrate that the proposed approach can
effectively improve feature representation learning on sparse features.

本文从图学习的角度，利用 Hypergraph Transformer 模型，通过在构造的超图上传递信息，得出在高维稀疏数据的特征表征学习中的相关性，从而更好地解决相关性的问题。