Learning From Simplicial Data Based on Random Walks and 1D Convolutions

arXiv:2404.03434v1 Announce Type: new
Abstract: Triggered by limitations of graph-based deep learning methods in terms of computational expressivity and model flexibility, recent years have seen a surge of interest in computational models that operate on higher-order topological domains such as hypergraphs and simplicial complexes. While the increased expressivity of these models can indeed lead to a better classification performance and a more faithful representation of the underlying system, the computational cost of these higher-order models can increase dramatically. To this …

abstract arxiv computational cs.lg data deep learning domains flexibility graph graph-based limitations random terms type