Hypergraphs are used to model higher-order interactions amongst agents and
there exist many practically relevant instances of hypergraph datasets. To
enable efficient processing of hypergraph-structured data, several hypergraph
neural network platforms have been proposed for learning hypergraph properties
and structure, with a special focus on node classification. However, almost all
existing methods use heuristic propagation rules and offer suboptimal
performance on many datasets. We propose AllSet, a new hypergraph neural
network paradigm that represents a highly general framework for (hyper)graph
neural networks and for the first time implements hypergraph neural network
layers as compositions of two multiset functions that can be efficiently
learned for each task and each dataset. Furthermore, AllSet draws on new
connections between hypergraph neural networks and recent advances in deep
learning of multiset functions. In particular, the proposed architecture
utilizes Deep Sets and Set Transformer architectures that allow for significant
modeling flexibility and offer high expressive power. To evaluate the
performance of AllSet, we conduct the most extensive experiments to date
involving ten known benchmarking datasets and three newly curated datasets that
represent significant challenges for hypergraph node classification. The
results demonstrate that AllSet has the unique ability to consistently either
match or outperform all other hypergraph neural networks across the tested
datasets.

AllSet 是一种新的超图神经网络范式，利用多重集合函数以及 Deep Sets 和 Set Transformer 架构实现超图神经网络层，并在 10 个基准数据集和 3 个新数据集上展开广泛实验，结果表明 AllSet 相较于其他超图神经网络具有更高的模型灵活性和泛化能力，在分类任务上的表现更优秀。