Joint Graph and Vertex Importance Learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 15 Mar 2023.
Builds on
14 of 25 references.
References · 14
- 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 409
- 2018Learning graphs from data: A signal representation perspectivecited by 119
- 2018Connecting the Dots: Identifying Network Structure via Graph Signal Processingcited by 111
- 2016Stationary Graph Processes and Spectral Estimationcited by 35
- 2014Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov Random fieldscited by 19
- 2017Learning Graphs with Monotone Topology Properties and Multiple Connected Componentscited by 13
- 2018Irregularity-Aware Graph Fourier Transformscited by 8
- 2020Region adaptive graph fourier transform for 3d point cloudscited by 8
- 2020An efficient algorithm for graph Laplacian optimization based on effective resistancescited by 4
- 2021Laplacian Constrained Precision Matrix Estimation: Existence and High Dimensional Consistencycited by 4
- 2022Two Channel Filter Banks on Arbitrary Graphs with Positive Semi Definite Variation Operatorscited by 3
- 2020Perceptually inspired weighted MSE optimization using irregularity-aware graph Fourier transformcited by 2
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Citing papers per year: 2024: 1.
1 paper.