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- W4224903912 abstract "Graph representation learning methods have mostly been limited to the modelling of node-wise interactions. Recently, there has been an increased interest in understanding how higher-order structures can be utilised to further enhance the learning abilities of graph neural networks (GNNs) in combinatorial spaces. Simplicial Neural Networks (SNNs) naturally model these interactions by performing message passing on simplicial complexes, higher-dimensional generalisations of graphs. Nonetheless, the computations performed by most existent SNNs are strictly tied to the combinatorial structure of the complex. Leveraging the success of attention mechanisms in structured domains, we propose Simplicial Attention Networks (SAT), a new type of simplicial network that dynamically weighs the interactions between neighbouring simplicies and can readily adapt to novel structures. Additionally, we propose a signed attention mechanism that makes SAT orientation equivariant, a desirable property for models operating on (co)chain complexes. We demonstrate that SAT outperforms existent convolutional SNNs and GNNs in two image and trajectory classification tasks." @default.
- W4224903912 created "2022-04-27" @default.
- W4224903912 creator A5053576615 @default.
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- W4224903912 date "2022-04-20" @default.
- W4224903912 modified "2023-09-24" @default.
- W4224903912 title "Simplicial Attention Networks" @default.
- W4224903912 doi "https://doi.org/10.48550/arxiv.2204.09455" @default.
- W4224903912 hasPublicationYear "2022" @default.
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