Joint Graph and Vertex Importance Learning

• URL: http://arxiv.org/abs/2303.08552v1
• Date: Wed, 15 Mar 2023 12:12:13 GMT
• Title: Joint Graph and Vertex Importance Learning
• Authors: Benjamin Girault, Eduardo Pavez, Antonio Ortega
• Abstract summary: We propose a novel method to learn a graph with smaller edge weight upper bounds compared to Laplacian approaches. Experimentally, our approach yields much sparser graphs compared to a Laplacian approach, with a more interpretable model.
• Score: 47.249968772606145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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