  Hypergraphs (i.e., sets of hyperedges) naturally represent group relations
(e.g., researchers co-authoring a paper and ingredients used together in a
recipe), each of which corresponds to a hyperedge (i.e., a subset of nodes).
Predicting future or missing hyperedges bears significant implications for many
applications (e.g., collaboration and recipe recommendation). What makes
hyperedge prediction particularly challenging is the vast number of
non-hyperedge subsets, which grows exponentially with the number of nodes.
Since it is prohibitive to use all of them as negative examples for model
training, it is inevitable to sample a very small portion of them, and to this
end, heuristic sampling schemes have been employed. However, trained models
suffer from poor generalization capability for examples of different natures.
In this paper, we propose AHP, an adversarial training-based
hyperedge-prediction method. It learns to sample negative examples without
relying on any heuristic schemes. Using six real hypergraphs, we show that AHP
generalizes better to negative examples of various natures. It yields up to
28.2% higher AUROC than the best existing methods and often even outperforms
its variants with sampling schemes tailored to test sets.
