Graph Learning Over Polytopic Uncertain Graph

Masako Kishida

doi:10.1109/LSP.2025.3531218

Graph Learning Over Polytopic Uncertain Graph

2025-01-01

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DOI：10.1109/LSP.2025.3531218

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This letter introduces a graph learning approach leveraging prior knowledge of graph topology. For this, we integrate the concept of polytopic uncertainty into existing approaches that learn graph Laplacians and adjacency matrices, constraining the solution space to a polytopic set. Our approach offers improved accuracy with reduced computational cost by focusing on a smaller solution space, effectively excluding implausible topologies. Numerical experiments demonstrate superior learned graph quality compared to existing approaches across various signal models and noise levels.

Keywords:

Laplace equations

Topology

Uncertainty

Accuracy

Optimization

Computational efficiency

Vectors

Noise level

Signal to noise ratio

Matrix converters

Graph learning

graph signal processing

uncertainty

Graph Learning Over Polytopic Uncertain Graph

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