Abstract
Motifs are overrepresented and statistically significant sub-patterns in a network, whose identification is relevant to uncover its underlying functional units. Recently, its extraction has been performed on higher-order networks, but due to the complexity arising from polyadic interactions, and the similarity with known computationally hard problems, its practical application is limited. Our main contribution is a novel approach for hyper-subgraph census and higher-order motif discovery, allowing for motifs with sizes 3 or 4 to be found efficiently, in real-world scenarios. It is consistently an order of magnitude faster than a baseline state-of-art method, while using less memory and supporting a wider range of base algorithms.
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Notes
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All the hypergraph images were created using [8].
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Nóbrega, D., Ribeiro, P. (2024). Computing Motifs in Hypergraphs. In: Botta, F., Macedo, M., Barbosa, H., Menezes, R. (eds) Complex Networks XV. CompleNet-Live 2024. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-57515-0_5
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