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Graph representation learning for hypergraphs can be used to extract patternsamong higher-order interactions that are critically important in many realworld problems. Current approaches designed for hypergraphs, however, areunable to handle different types of hypergraphs and are typically not genericfor various learning tasks. Indeed, models that can predict variable-sizedheterogeneous hyperedges have not been available. Here we develop a newself-attention based graph neural network called Hyper-SAGNN applicable tohomogeneous and heterogeneous hypergraphs with variable hyperedge sizes. Weperform extensive evaluations on multiple datasets, including four benchmarknetwork datasets and two single-cell Hi-C datasets in genomics. We demonstratethat Hyper-SAGNN significantly outperforms the state-of-the-art methods ontraditional tasks while also achieving great performance on a new task calledoutsider identification. Hyper-SAGNN will be useful for graph representationlearning to uncover complex higher-order interactions in differentapplications.

Hyper-SAGNN: a self-attention based graph neural network for hypergraphs