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arXiv · 2303.08552

Joint Graph and Vertex Importance Learning

Benjamin Girault·Eduardo Pavez·Antonio Ortega
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Abstract

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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Benjamin Girault, Eduardo Pavez, Antonio Ortega. 2023-03-15. Joint Graph and Vertex Importance Learning. https://arxiv.org/abs/2303.08552

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