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Hypergraph $p$-Laplacians and Scale Spaces

Authors :
Fazeny, Ariane
Tenbrinck, Daniel
Lukin, Kseniia
Burger, Martin
Source :
In: Scale Space and Variational Methods in Computer Vision. SSVM 2023. Lecture Notes in Computer Science, vol 14009. Springer, Cham (2023)
Publication Year :
2023

Abstract

This paper introduces gradient, adjoint, and $p$-Laplacian definitions for oriented hypergraphs as well as differential and averaging operators for unoriented hypergraphs. These definitions are used to define gradient flows in the form of diffusion equations with applications in modelling group dynamics and information flow in social networks as well as performing local and non-local image processing.<br />Comment: 33 pages, 5 figures, submitted to Scale Space and Variational Methods, part of it published in International Conference on Scale Space and Variational Methods in Computer Vision proceedings

Details

Database :
arXiv
Journal :
In: Scale Space and Variational Methods in Computer Vision. SSVM 2023. Lecture Notes in Computer Science, vol 14009. Springer, Cham (2023)
Publication Type :
Report
Accession number :
edsarx.2309.15419
Document Type :
Working Paper
Full Text :
https://doi.org/10.1007/978-3-031-31975-4_52