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Topological Deep Learning

Topological Deep Learning (TDL) is a framework that extends deep learning beyond graphs to higher-order topological domains such as simplicial complexes, cell complexes, and hypergraphs. Formalized by Hajij et al. (2023), TDL provides a unified mathematical language for defining message-passing schemes across cells of different ranks, enabling neural networks to model multi-way interactions that pairwise graph edges cannot capture. It is relevant to researchers working with relational, geometric, or biological data exhibiting group-level dependencies.

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Rekod kaedah taksonomik · ml-model / topology

Hajij, M., et al. (2023). Topological deep learning: Going beyond graph data. arXiv preprint. · URL

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