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Chemically inspired Erd\H{o}s-R\'enyi oriented hypergraphs
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High-order structures have been recognised as suitable models for systems going beyond the binary relationships for which graph models are appropriate. Despite their importance and surge in research on these structures, their random cases have been only recently become subjects of interest. One of these high-order structures is the oriented hypergraph, which relates couples of subsets of an arbitrary number of vertices. Here we develop the Erd\H{o}s-R\'enyi model for oriented hypergraphs, which corresponds to the random realisation of oriented hyperedges of the complete oriented hypergraph. A particular feature of random oriented hypergraphs is that the ratio between their expected number of oriented hyperedges and their expected degree or size is 3/2 for large number of vertices. We highlight the suitability of oriented hypergraphs for modelling large collections of chemical reactions and the importance of random oriented hypergraphs to analyse the unfolding of chemistry.
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Enhancing the Utility of Higher-Order Information in Relational Learning
Graph-level GNNs with new hypergraph-based encodings beat hypergraph-specific GNNs on several benchmarks, and the encodings provably increase expressivity beyond graph-level encodings.
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