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Pré-Publication, Document De Travail (Preprint/Prepublication) Année : 2023

Joint Graph and Vertex Importance Learning

Résumé

In this paper, we explore the topic of graph learning from the perspective of the Irregularity-Aware Graph Fourier Transform, with the goal of learning the graph signal space inner product to better model data. We propose a novel method to learn a graph with smaller edge weight upper bounds compared to combinatorial Laplacian approaches. Experimentally, our approach yields much sparser graphs compared to a combinatorial Laplacian approach, with a more interpretable model.
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Dates et versions

hal-04025968 , version 1 (13-03-2023)
hal-04025968 , version 2 (08-06-2023)

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  • HAL Id : hal-04025968 , version 1

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Benjamin Girault, Eduardo Pavez, Antonio Ortega. Joint Graph and Vertex Importance Learning. 2023. ⟨hal-04025968v1⟩
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