Joint Graph and Vertex Importance Learning

Benjamin Girault (1, 2) , Eduardo Pavez (3) , Antonio Ortega (3)

1 CREST - Centre de Recherche en Économie et Statistique
2 ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz]
3 Signal and Image Processing Institute

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.

Mots clés: graph learning; graph signal Hilbert space; graph signal processing

Domaines: Traitement du signal et de l'image [eess.SP]; Apprentissage [cs.LG]

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