Xingzhi Sun, Charles Xu, João F. Rocha, Chen Liu, Benjamin Hollander-Bodie, Laney Goldman, Marcello DiStasio, Michael Perlmutter, Smita Krishnaswamy
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引用次数: 0
Abstract
In many data-driven applications, higher-order relationships among multiple
objects are essential in capturing complex interactions. Hypergraphs, which
generalize graphs by allowing edges to connect any number of nodes, provide a
flexible and powerful framework for modeling such higher-order relationships.
In this work, we introduce hypergraph diffusion wavelets and describe their
favorable spectral and spatial properties. We demonstrate their utility for
biomedical discovery in spatially resolved transcriptomics by applying the
method to represent disease-relevant cellular niches for Alzheimer's disease.
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