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

Recent advances in the field of network embedding have shown that
low-dimensional network representation is playing a critical role in network
analysis. Most existing network embedding methods encode the local proximity of
a node, such as the first- and second-order proximities. While being efficient,
these methods are short of leveraging the global structural information between
nodes distant from each other. In addition, most existing methods learn
embeddings on one single fixed network, and thus cannot be generalized to
unseen nodes or networks without retraining. In this paper we present SPINE, a
method that can jointly capture the local proximity and proximities at any
distance, while being inductive to efficiently deal with unseen nodes or
networks. Extensive experimental results on benchmark datasets demonstrate the
superiority of the proposed framework over the state of the art.

SPINE 是一种具有归纳性的网络嵌入方法，可以同时捕捉节点之间的局部距离和任意距离的结构信息，并能够有效地处理未见节点。该方法在基准数据集上取得了超越现有技术的结果。