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Hypergraphs are used to model higher-order interactions amongst agents andthere exist many practically relevant instances of hypergraph datasets. Toenable efficient processing of hypergraph-structured data, several hypergraphneural network platforms have been proposed for learning hypergraph propertiesand structure, with a special focus on node classification. However, almost allexisting methods use heuristic propagation rules and offer suboptimalperformance on many datasets. We propose AllSet, a new hypergraph neuralnetwork paradigm that represents a highly general framework for (hyper)graphneural networks and for the first time implements hypergraph neural networklayers as compositions of two multiset functions that can be efficientlylearned for each task and each dataset. Furthermore, AllSet draws on newconnections between hypergraph neural networks and recent advances in deeplearning of multiset functions. In particular, the proposed architectureutilizes Deep Sets and Set Transformer architectures that allow for significantmodeling flexibility and offer high expressive power. To evaluate theperformance of AllSet, we conduct the most extensive experiments to dateinvolving ten known benchmarking datasets and three newly curated datasets thatrepresent significant challenges for hypergraph node classification. Theresults demonstrate that AllSet has the unique ability to consistently eithermatch or outperform all other hypergraph neural networks across the testeddatasets.

You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks