  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.
