Feb. 9, 2024, 5:42 a.m. | Bohan Tang Zexi Liu Keyue Jiang Siheng Chen Xiaowen Dong

cs.LG updates on arXiv.org arxiv.org

Hypergraphs, with hyperedges connecting more than two nodes, are key for modelling higher-order interactions in real-world data. The success of graph neural networks (GNNs) reveals the capability of neural networks to process data with pairwise interactions. This inspires the usage of neural networks for data with higher-order interactions, thereby leading to the development of hypergraph neural networks (HyperGNNs). GNNs and HyperGNNs are typically considered distinct since they are designed for data on different geometric topologies. However, in this paper, we …

capability classification cs.ai cs.lg data development eess.sp gnns graph graph neural networks hypergraph interactions key modelling networks neural networks node nodes process stat.ml success usage world

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